Using communicative discourse trees to detect distributed incompetence

ABSTRACT

Techniques are disclosed for detecting distributed incompetence in text of a conversation using communicative discourse trees and then inserting an automatic response from an autonomous agent (chatbot) or other entity. For example, a computing system generates a communicative discourse tree from utterances from multiple agents to a user. The computing system obtains a prediction of whether the text includes distributed incompetence by applying a trained predictive model to the communicative discourse tree. Based on the detection, the computing system generates an updated response to a user device.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation in part of Ser. No. 16/260,939, filedJan. 29, 2019, which claims the benefit of 62/623,999, filed Jan. 30,2018, and 62/646,795, filed Mar. 22, 2018, and is a continuation in partof Ser. No. 16/010,091, filed Jun. 15, 2018, which claims the benefit of62/520,456, filed Jun. 15, 2017, and is a continuation in part of Ser.No. 15/975,683, filed May 9, 2018, which claims the benefit of62/504,377, filed May 10, 2017, all of which are incorporated byreference in their entireties. This application claims priority from62/822,128 filed Mar. 22, 2019 and 62/892,765 filed Aug. 28, 2019, whichare both incorporated by reference in their entireties.

TECHNICAL FIELD

This disclosure is generally concerned with linguistics. Morespecifically, this disclosure relates to using communicative discoursetrees to determine a presence of distributed incompetence.

BACKGROUND

Linguistics is the scientific study of language. One aspect oflinguistics is the application of computer science to human naturallanguages. Computer-enabled analysis of language discourse facilitatesnumerous applications such as automated agents that can answer questionsfrom users. The use of autonomous agents (“chatbots”) to answerquestions, facilitate discussion, manage dialogues, and provide socialpromotion is increasingly popular.

BRIEF SUMMARY

Generally, systems, devices, and methods of the present invention arerelated to communicative discourse trees. For example, an applicationcan determine a presence of distributed incompetence in text, therebyenabling more accurate and realistic autonomous agents.

In an aspect, a computer-implemented method determines a presence ofdistributed incompetence by analyzing a communicative discourse tree.The method includes accessing a body of text including fragments. Atleast one fragment includes a verb and a words, each word including arole of the words within the fragment. Each fragment is an elementarydiscourse unit. The method includes generating a discourse tree thatrepresents rhetorical relationships between the fragments. The discoursetree includes nodes. Each nonterminal node represents a rhetoricalrelationship between two of the fragments and each terminal node of thenodes of the discourse tree is associated with one of the fragments. Themethod includes matching each fragment that has a verb to a verbsignature. The method includes building a communicative discourse treeby augmenting the fragments in the discourse tree with the respectivematched verb signatures. The method includes computing a probability ofa presence of distributed incompetence in the body of text by applying apredictive model to the communicative discourse tree. The predictivemodel is trained to detect a level of distributed incompetence. Themethod includes identifying the body of text as containing distributedincompetence responsive to determining that the probability is past athreshold. The method includes generating a response based on theidentification of distributed incompetence and inserting the generatedresponse into a conversation associated with the body of text.

In an aspect, the method further includes identifying, via thepredictive model and in the communicative discourse tree, a firstcommunicative action that identifies a first entity as a first actor anda second entity as a first recipient of the first communicative action.The method further includes identifying, via the predictive model and inthe communicative discourse tree, a second communicative action thatidentifies the second entity as a second actor and the first entity as asecond recipient of the second communicative action.

In an aspect, the method further includes identifying, via thepredictive model and in the communicative discourse tree, a firstcommunicative action that attributes an entity to a first entity. Thefirst communicative action can be associated with an attributionrhetorical relation. The method further includes identifying, via thepredictive model and in the communicative discourse tree, a secondcommunicative action that attributes the entity to a second actor.

In an aspect, the method further includes identifying, via thepredictive model and in the communicative discourse tree, a firstcommunicative action that is of class “deny” and identifies a firstactor.

In an aspect, the matching includes accessing verb signatures. Each verbsignature includes the verb of a respective fragment and a sequence ofthematic roles. The thematic roles describe a relationship between theverb and related words. The matching further includes determining, foreach verb signature, a thematic roles of the respective signature thatmatches a role of a word in the fragment. The matching further includesselecting a particular verb signature from the verb signatures based onthe particular verb signature including a highest number of matches. Thematching further includes associating the particular verb signature withthe fragment.

In an aspect, the associating further includes identifying each of thethematic roles in the particular verb signature. The associating furtherincludes matching, for each of the thematic roles in the particular verbsignature, a corresponding word in the fragment to the thematic role.

In an aspect, the verb is a communicative verb.

In an aspect, the each verb signature of the verb signatures includesone of an adverb, a noun phrase, or a noun.

The above methods can be implemented as tangible computer-readable mediaand/or operating within a computer processor and attached memory.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary computing environment in accordance with anaspect.

FIG. 2 depicts an example of a discourse tree in accordance with anaspect.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect.

FIG. 4 depicts illustrative schemas in accordance with an aspect.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect.

FIG. 7 depicts an exemplary DT for an example request about property taxin accordance with an aspect.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7.

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect.

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect.

FIG. 11 illustrates a communicative discourse tree for a claim of afirst agent in accordance with an aspect.

FIG. 12 illustrates a communicative discourse tree for a claim of asecond agent in accordance with an aspect.

FIG. 13 illustrates a communicative discourse tree for a claim of athird agent in accordance with an aspect.

FIG. 14 illustrates parse thickets in accordance with an aspect.

FIG. 15 illustrates an exemplary process for building a communicativediscourse tree in accordance with an aspect.

FIG. 16 illustrates a discourse tree and scenario graph in accordancewith an aspect.

FIG. 17 illustrates forming a request-response pair in accordance withan aspect.

FIG. 18 illustrates a maximal common sub-communicative discourse tree inaccordance with an aspect.

FIG. 19 illustrates a tree in a kernel learning format for acommunicative discourse tree in accordance with an aspect.

FIG. 20 illustrates an exemplary process used to implement a classifierin accordance with an aspect.

FIG. 21 illustrates a chat bot commenting on a posting in accordancewith an aspect.

FIG. 22 illustrates a chat bot commenting on a posting in accordancewith an aspect.

FIG. 23 illustrates a discourse tree for algorithm text in accordancewith an aspect.

FIG. 24 illustrates annotated sentences in accordance with an aspect.

FIG. 25 illustrates annotated sentences in accordance with an aspect.

FIG. 26 illustrates discourse acts of a dialogue in accordance with anaspect.

FIG. 27 illustrates discourse acts of a dialogue in accordance with anaspect.

FIG. 28 depicts an exemplary communicative discourse tree in accordancewith an aspect.

FIG. 29 depicts an exemplary communicative discourse tree in accordancewith an aspect.

FIG. 30 depicts an exemplary communicative discourse tree in accordancewith an aspect.

FIG. 31 depicts an exemplary communicative discourse tree in accordancewith an aspect.

FIG. 32 depicts an example communicative discourse tree in accordancewith an aspect.

FIG. 33 depicts an example communicative discourse tree in accordancewith an aspect.

FIG. 34 depicts an example communicative discourse tree in accordancewith an aspect.

FIG. 35 depicts an example communicative discourse tree in accordancewith an aspect.

FIG. 36 depicts an exemplary process for using machine learning todetermine argumentation in accordance with an aspect.

FIG. 37 is a fragment of a discourse tree in accordance with an aspect.

FIG. 38 depicts a discourse tree for a borderline review in accordancewith an aspect.

FIG. 39 depicts a discourse tree for a sentence showing compositionalsemantic approach to sentiment analysis in accordance with an aspect.

FIG. 40 depicts an exemplary method for validating arguments inaccordance with an aspect.

FIG. 41 depicts an exemplary communicative discourse tree for anargument in accordance with an aspect.

FIG. 42 depicts an exemplary method for validating arguments usingdefeasible logic programming in accordance with an aspect.

FIG. 43 depicts an exemplary dialectic tree in accordance with anaspect.

FIG. 44 depicts an example of an interactive computing session inaccordance with an aspect.

FIG. 45 depicts an exemplary communicative discourse tree in accordancewith an aspect.

FIG. 46 depicts a first exemplary communicative discourse tree thatincludes features indicative of distributed incompetence for an argumentin accordance with an aspect.

FIG. 47 depicts a second exemplary communicative discourse tree thatincludes features indicative of distributed incompetence for an argumentin accordance with an aspect.

FIG. 48 depicts a third exemplary communicative discourse tree thatincludes features indicative of distributed incompetence for an argumentin accordance with an aspect.

FIG. 49 is a flowchart of an exemplary process for detecting distributedincompetence in accordance with an aspect.

FIG. 50 depicts a simplified diagram of a distributed system forimplementing one of the aspects.

FIG. 51 is a simplified block diagram of components of a systemenvironment by which services provided by the components of an aspectsystem may be offered as cloud services in accordance with an aspect.

FIG. 52 illustrates an exemplary computer system, in which variousaspects of the present invention may be implemented.

DETAILED DESCRIPTION

Aspects disclosed herein provide technical improvements to the area ofcomputer-implemented linguistics. More specifically, aspects of thepresent disclosure use communicative discourse trees to detectdistributed incompetence present in a body of text. Distributedincompetence is a type of organizational incompetence in which a team,computationally represented as a multi-agent system, collectively failsto address a problem. Communicative discourse trees (“CDTs”) includediscourse trees that are supplemented with communicative actions. CDTsare used to represent text that may or may not indicate distributedincompetence. A communicative action is a cooperative action undertakenby individuals based on mutual deliberation and argumentation.

In a multi-agent system, distributed knowledge can be all the knowledgethat a team of agents can leverage in solving a particular problem.Distributed knowledge includes what is known about what the knowledge ofeach team member, including the knowledge that a community might bringto solve a problem. For example, distributed knowledge and the wisdom ofthe crowd have demonstrated their superiority over problem solving ofindividual agents in a number of domains. In contrast, distributedincompetence is the opposite of distributed knowledge. An example ofdistributed incompetence is when a given agent A refers a user toanother agent B for help with a given problem, B further refers to C andso forth, and yet the user's problem is still not solved. Agents caninclude machine or human agents.

Technical advantages of some aspects include improved autonomous agentsthat can detect distributed incompetence in text using CDTs. CDTs can beleveraged in conjunction with machine-learning techniques, over a richerfeatures set than rhetoric relations and language syntax and informationcontained within elementary discourse units (EDUs) alone. Disclosedsolutions improve over traditional keyword-based solutions.

Certain Definitions

As used herein, “rhetorical structure theory” is an area of research andstudy that provided a theoretical basis upon which the coherence of adiscourse could be analyzed.

As used herein, “discourse tree” or “DT” refers to a structure thatrepresents the rhetorical relations for a sentence of part of asentence.

As used herein, a “rhetorical relation,” “rhetorical relationship,” or“coherence relation” or “discourse relation” refers to how two segmentsof discourse are logically connected to one another. Examples ofrhetorical relations include elaboration, contrast, and attribution.

As used herein, a “sentence fragment,” or “fragment” is a part of asentence that can be divided from the rest of the sentence. A fragmentis an elementary discourse unit. For example, for the sentence “Dutchaccident investigators say that evidence points to pro-Russian rebels asbeing responsible for shooting down the plane,” two fragments are “Dutchaccident investigators say that evidence points to pro-Russian rebels”and “as being responsible for shooting down the plane.” A fragment can,but need not, include a verb.

As used herein, “signature” or “frame” refers to a property of a verb ina fragment. Each signature can include one or more thematic roles. Forexample, for the fragment “Dutch accident investigators say thatevidence points to pro-Russian rebels,” the verb is “say” and thesignature of this particular use of the verb “say” could be “agent verbtopic” where “investigators” is the agent and “evidence” is the topic.

As used herein, “thematic role” refers to components of a signature usedto describe a role of one or more words. Continuing the previousexample, “agent” and “topic” are thematic roles.

As used herein, “nuclearity” refers to which text segment, fragment, orspan, is more central to a writer's purpose. The nucleus is the morecentral span, and the satellite is the less central one.

As used herein, “coherency” refers to the linking together of tworhetorical relations.

As used herein, “communicative verb” is a verb that indicatescommunication. For example, the verb “deny” is a communicative verb.

As used herein, “communicative action” describes an action performed byone or more agents and the subjects of the agents.

As used herein, “claim” is an assertion of truth of something. Forexample, a claim could be “I am not responsible for paying rent thismonth” or “the rent is late.”

As used herein, an “argument” is a reason or set of reasons set forth tosupport a claim. An example argument for the above claim is “thenecessary repairs were not completed.”

As used herein, a “argument validity” or “validity” refers to whether anargument that supports a claim is internally and consistent. Internalconsistency refers to whether the argument is consistent with itself,e.g., does not contain two contradictory statements. Externalconsistency refers to whether an argument is consistent with known factsand rules.

As used herein, a “logic system” or “logic program” is a set ofinstructions, rules, facts, and other information that can representargumentation of a particular claim. Solving the logic system results ina determination of whether the argumentation is valid.

As used herein, a “dialectic tree” is a tree that represents individualarguments. A dialectic tree is solved to determine a truth or falsity ofa claim supported by the individual arguments. Evaluating a dialectictree involves determining validity of the individual arguments.

Turning now to the Figures, FIG. 1 depicts an exemplary computingenvironment in accordance with an aspect of the present disclosure. FIG.1 depicts one or more of computing device 101, display 130, network 150,user device 160, and external text corpus 170. In the example depictedin FIG. 1, computing device 101 communicates over network 150 with userdevice 160, interacting with user device 160, displaying theinteractions 131-132 on display 130, and detecting distributedincompetence in the interactions. If distributed incompetence isdetected, computing device 101 can adjust a response, e.g., interaction132, provided to user device 160. In some aspects, computing device 101outputs distributed incompetence indicator 165, which can be provided toanother computing system or device.

User device 160 can be any mobile device such as a mobile phone, smartphone, tablet, laptop, smart watch, and the like. Computing device 101can process text from any source including user device 160. Examples ofinput text include customer support log that includes interactionsbetween agents and specifications used to guide the behavior of agents.Agent specifications include a desired agent response for a given userresponse. One or more agent specifications can reveal distributedincompetence.

Computing device 101 includes one or more of classification application102, answer database 105, classifier 120, and training data 125.Classification application 102 can interact with user device 160 byreceiving questions from user device 160 and answering those questions.An example of a process for detecting distributed incompetence isdiscussed further with respect to FIG. 49. Examples of computing device101 are distributed system 5000 and client computing devices 5002, 5004,5006, and 5008. Examples of user device 160 include client computingdevices 5002, 5004, 5006, and 5008.

In the example depicted in FIG. 1, computing device 101 receivesinteraction 131, which includes the following dialogue:

User: I have been charged a non-sufficient funds fee. I'd like itreversed please.

Agent 1: I cannot reverse it, but management can. I'll pass you to them.

User: I have been charged a non-sufficient funds fee. I'd like itreversed please.

Agent 2: I am unable to reverse the non-sufficient funds fee.

As can be seen in the above text, the user is not satisfied as obtaininga consistent answer about the non-sufficient funds fee is difficult. Inparticular, agent 1 and agent 2 appear to collectively be illustratingdistributed incompetence. Classification application 102, usingdiscourse-based techniques can determine distributed incompetence inthis text. As further discussed herein, distributed incompetence can beidentified by one or more features within the text.

Machine-learning techniques can be used to determine distributedincompetence. For example, classification application 102 can provide acommunicative discourse tree to a classifier 120. Classifier 120compares the communicative discourse tree with communicative discoursetrees identified in a training set as positive (including distributedincompetence) or negative (not including distributed incompetence).Classification application 102 receives a prediction of whetherdistributed incompetence is present from classifier 120. Classificationapplication 102 provides the prediction as distributed incompetenceindicator 165.

Rhetoric Structure Theory and Discourse Trees

Linguistics is the scientific study of language. For example,linguistics can include the structure of a sentence (syntax), e.g.,subject-verb-object, the meaning of a sentence (semantics), e.g. dogbites man vs. man bites dog, and what speakers do in conversation, i.e.,discourse analysis or the analysis of language beyond the sentence.

The theoretical underpinnings of discourse, Rhetoric Structure Theory(RST), can be attributed to Mann, William and Thompson, Sandra,“Rhetorical structure theory: A Theory of Text organization,”Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243-281,1988. Similar to how the syntax and semantics of programming languagetheory helped enable modern software compilers, RST helped enabled theanalysis of discourse. More specifically RST posits structural blocks onat least two levels, a first level such as nuclearity and rhetoricalrelations, and a second level of structures or schemas. Discourseparsers or other computer software can parse text into a discourse tree.

Rhetoric Structure Theory models logical organization of text, astructure employed by a writer, relying on relations between parts oftext. RST simulates text coherence by forming a hierarchical, connectedstructure of texts via discourse trees. Rhetoric relations are splitinto the classes of coordinate and subordinate; these relations holdacross two or more text spans and therefore implement coherence. Thesetext spans are called elementary discourse units (EDUs). Clauses in asentence and sentences in a text are logically connected by the author.The meaning of a given sentence is related to that of the previous andthe following sentences. This logical relation between clauses is calledthe coherence structure of the text. RST is one of the most populartheories of discourse, being based on a tree-like discourse structure,discourse trees (DTs). The leaves of a DT correspond to EDUs, thecontiguous atomic text spans. Adjacent EDUs are connected by coherencerelations (e.g., Attribution, Sequence), forming higher-level discourseunits. These units are then also subject to this relation linking. EDUslinked by a relation are then differentiated based on their relativeimportance: nuclei are the core parts of the relation, while satellitesare peripheral ones. As discussed, in order to determine accuraterequest-response pairs, both topic and rhetorical agreement areanalyzed. When a speaker answers a question, such as a phrase or asentence, the speaker's answer should address the topic of thisquestion. In the case of an implicit formulation of a question, via aseed text of a message, an appropriate answer is expected not onlymaintain a topic, but also match the generalized epistemic state of thisseed.

Rhetoric Relations

As discussed, aspects described herein use communicative discoursetrees. Rhetorical relations can be described in different ways. Forexample, Mann and Thompson describe twenty-three possible relations. C.Mann, William & Thompson, Sandra. (1987) (“Mann and Thompson”).Rhetorical Structure Theory: A Theory of Text Organization. Othernumbers of relations are possible.

Relation Name Nucleus Satellite Antithesis ideas favored by the ideasdisfavored by the author author Background text whose understanding textfor facilitating understanding is being facilitated Circumstance textexpressing the events an interpretive context of situation or or ideasoccurring in the time interpretive context Concession situation affirmedby situation which is apparently author inconsistent but also affirmedby author Condition action or situation whose conditioning situationoccurrence results from the occurrence of the conditioning situationElaboration basic information additional information Enablement anaction information intended to aid the reader in performing an actionEvaluation a situation an evaluative comment about the situationEvidence a claim information intended to increase the reader's belief inthe claim Interpretation a situation an interpretation of the situationJustify text information supporting the writer's right to express thetext Motivation an action information intended to increase the reader'sdesire to perform the action Non- a situation another situation whichcauses that volitional one, but not by anyone's deliberate Cause actionNon- situation another situation which is caused by volitional that one,but not by anyone's Result deliberate action Otherwise action orsituation whose conditioning situation (anti occurrence results fromconditional) the lack of occurrence of the conditioning situationPurpose an intended situation the intent behind the situationRestatement a situation a reexpression of the situation Solutionhood asituation or method a question, request, problem, or supporting full orpartial other expressed need satisfaction of the need Summary text ashort summary of that text Volitional a situation another situationwhich causes that Cause one, by someone's deliberate action Volitional asituation another situation which is caused by Result that one, bysomeone's deliberate action

Some empirical studies postulate that the majority of text is structuredusing nucleus-satellite relations. See Mann and Thompson. But otherrelations do not carry a definite selection of a nucleus. Examples ofsuch relations are shown below.

Relation Name Span Other Span Contrast One alternate The other alternateJoint (unconstrained) (unconstrained) List An item A next item SequenceAn item A next item

FIG. 2 depicts an example of a discourse tree in accordance with anaspect. FIG. 2 includes discourse tree 200. Discourse tree includes textspan 201, text span 202, text span 203, relation 210 and relation 211.The numbers in FIG. 2 correspond to the three text spans. FIG. 3corresponds to the following example text with three text spans numbered1, 2, 3:

1. Honolulu, Hi. will be site of the 2017 Conference on Hawaiian History

2. It is expected that 200 historians from the U.S. and Asia will attend

3. The conference will be concerned with how the Polynesians sailed toHawaii

For example, relation 210, or elaboration, describes the relationshipbetween text span 201 and text span 202. Relation 228 depicts therelationship, elaboration, between text span 203 and 204. As depicted,text spans 202 and 203 elaborate further on text span 201. In the aboveexample, given a goal of notifying readers of a conference, text span 1is the nucleus. Text spans 2 and 3 provide more detail about theconference. In FIG. 2, a horizontal number, e.g., 1-3, 1, 2, 3 covers aspan of text (possibly made up of further spans); a vertical linesignals the nucleus or nuclei; and a curve represents a rhetoricrelation (elaboration) and the direction of the arrow points from thesatellite to the nucleus. If the text span only functions as a satelliteand not as a nuclei, then deleting the satellite would still leave acoherent text. If from FIG. 2 one deletes the nucleus, then text spans 2and 3 are difficult to understand.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect. FIG. 3 includes components 301 and 302, text spans 305-307,relation 310 and relation 311. Relation 310 depicts the relationship310, enablement, between components 306 and 305, and 307, and 305.Relation 311 depicts a relation, enablement, between components 302 and208. FIG. 3 refers to the following text spans:

1. The new Tech Report abstracts are now in the journal area of thelibrary near the abridged dictionary.

2. Please sign your name by any means that you would be interested inseeing.

3. Last day for sign-ups is 31 May.

As can be seen, relation 328 depicts the relationship between entity 307and 306, which is enablement. FIG. 3 illustrates that while nuclei canbe nested, there exists only one most nuclear text span.

Constructing a Discourse Tree

Discourse trees can be generated using different methods. A simpleexample of a method to construct a DT bottom up is:

(1) Divide the discourse text into units by:

-   -   (a) Unit size may vary, depending on the goals of the analysis    -   (b) Typically, units are clauses

(2) Examine each unit, and its neighbors. Is there a relation holdingbetween them?

(3) If yes, then mark that relation.

(4) If not, the unit might be at the boundary of a higher-levelrelation. Look at relations holding between larger units (spans).

(5) Continue until all the units in the text are accounted for.

Mann and Thompson also describe the second level of building blockstructures called schemas applications. In RST, rhetoric relations arenot mapped directly onto texts; they are fitted onto structures calledschema applications, and these in turn are fitted to text. Schemaapplications are derived from simpler structures called schemas (asshown by FIG. 4). Each schema indicates how a particular unit of text isdecomposed into other smaller text units. A rhetorical structure tree orDT is a hierarchical system of schema applications. A schema applicationlinks a number of consecutive text spans, and creates a complex textspan, which can in turn be linked by a higher-level schema application.RST asserts that the structure of every coherent discourse can bedescribed by a single rhetorical structure tree, whose top schemacreates a span encompassing the whole discourse.

FIG. 4 depicts illustrative schemas in accordance with an aspect. FIG. 4shows a joint schema is a list of items consisting of nuclei with nosatellites. FIG. 4 depicts schemas 401-406. Schema 401 depicts acircumstance relation between text spans 410 and 428. Scheme 402 depictsa sequence relation between text spans 420 and 421 and a sequencerelation between text spans 421 and 422. Schema 403 depicts a contrastrelation between text spans 430 and 431. Schema 404 depicts a jointrelationship between text spans 440 and 441. Schema 405 depicts amotivation relationship between 450 and 451, and an enablementrelationship between 452 and 451. Schema 406 depicts joint relationshipbetween text spans 460 and 462. An example of a joint scheme is shown inFIG. 4 for the three text spans below:

1. Skies will be partly sunny in the New York metropolitan area today.

2. It will be more humid, with temperatures in the middle 80's.

3. Tonight will be mostly cloudy, with the low temperature between 65and 70.

While FIGS. 2-4 depict some graphical representations of a discoursetree, other representations are possible.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect. As can be seen from FIG. 5, theleaves of a DT correspond to contiguous non-overlapping text spanscalled Elementary Discourse Units (EDUs). Adjacent EDUs are connected byrelations (e.g., elaboration, attribution . . . ) and form largerdiscourse units, which are also connected by relations. “Discourseanalysis in RST involves two sub-tasks: discourse segmentation is thetask of identifying the EDUs, and discourse parsing is the task oflinking the discourse units into a labeled tree.” See Joty, Shafiq R andGiuseppe Carenini, Raymond T Ng, and Yashar Mehdad. 2013. Combiningintra- and multi-sentential rhetorical parsing for document-leveldiscourse analysis. In ACL (1), pages 486-496.

FIG. 5 depicts text spans that are leaves, or terminal nodes, on thetree, each numbered in the order they appear in the full text, shown inFIG. 6. FIG. 5 includes tree 500. Tree 500 includes, for example, nodes501-507. The nodes indicate relationships. Nodes are non-terminal, suchas node 501, or terminal, such as nodes 502-507. As can be seen, nodes503 and 504 are related by a joint relationship. Nodes 502, 505, 506,and 508 are nuclei. The dotted lines indicate that the branch or textspan is a satellite. The relations are nodes in gray boxes.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect. FIG. 6 includes text 600 andtext sequences 602-604. Text 600 is presented in a manner more amenableto computer programming. Text sequence 602 corresponds to node 502,sequence 603 corresponds to node 503, and sequence 604 corresponds tonode 504. In FIG. 6, “N” indicates a nucleus and “S” indicates asatellite.

Examples of Discourse Parsers

Automatic discourse segmentation can be performed with differentmethods. For example, given a sentence, a segmentation model identifiesthe boundaries of the composite elementary discourse units by predictingwhether a boundary should be inserted before each particular token inthe sentence. For example, one framework considers each token in thesentence sequentially and independently. In this framework, thesegmentation model scans the sentence token by token, and uses a binaryclassifier, such as a support vector machine or logistic regression, topredict whether it is appropriate to insert a boundary before the tokenbeing examined. In another example, the task is a sequential labelingproblem. Once text is segmented into elementary discourse units,sentence-level discourse parsing can be performed to construct thediscourse tree. Machine learning techniques can be used.

In one aspect of the present invention, two Rhetorical Structure Theory(RST) discourse parsers are used: CoreNLPProcessor which relies onconstituent syntax, and FastNLPProcessor which uses dependency syntax.See Surdeanu, Mihai & Hicks, Thomas & Antonio Valenzuela-Escarcega,Marco. Two Practical Rhetorical Structure Theory Parsers. (2015).

In addition, the above two discourse parsers, i.e., CoreNLPProcessor andFastNLPProcessor use Natural Language Processing (NLP) for syntacticparsing. For example, the Stanford CoreNLP gives the base forms ofwords, their parts of speech, whether they are names of companies,people, etc., normalize dates, times, and numeric quantities, mark upthe structure of sentences in terms of phrases and syntacticdependencies, indicate which noun phrases refer to the same entities.Practically, RST is a still theory that may work in many cases ofdiscourse, but in some cases, it may not work. There are many variablesincluding, but not limited to, what EDU's are in a coherent text, i.e.,what discourse segmenters are used, what relations inventory is used andwhat relations are selected for the EDUs, the corpus of documents usedfor training and testing, and even what parsers are used. So forexample, in Surdeanu, et al., “Two Practical Rhetorical Structure TheoryParsers,” paper cited above, tests must be run on a particular corpususing specialized metrics to determine which parser gives betterperformance. Thus unlike computer language parsers which givepredictable results, discourse parsers (and segmenters) can giveunpredictable results depending on the training and/or test text corpus.Thus, discourse trees are a mixture of the predicable arts (e.g.,compilers) and the unpredictable arts (e.g., like chemistry wereexperimentation is needed to determine what combinations will give youthe desired results).

In order to objectively determine how good a Discourse analysis is, aseries of metrics are being used, e.g., Precision/Recall/F1 metrics fromDaniel Marcu, “The Theory and Practice of Discourse Parsing andSummarization,” MIT Press, (2000). Precision, or positive predictivevalue is the fraction of relevant instances among the retrievedinstances, while recall (also known as sensitivity) is the fraction ofrelevant instances that have been retrieved over the total amount ofrelevant instances. Both precision and recall are therefore based on anunderstanding and measure of relevance. Suppose a computer program forrecognizing dogs in photographs identifies eight dogs in a picturecontaining 12 dogs and some cats. Of the eight dogs identified, fiveactually are dogs (true positives), while the rest are cats (falsepositives). The program's precision is 5/8 while its recall is 5/12.When a search engine returns 30 pages only 20 of which were relevantwhile failing to return 40 additional relevant pages, its precision is20/30=2/3 while its recall is 20/60=1/3. Therefore, in this case,precision is ‘how useful the search results are’, and recall is ‘howcomplete the results are.’” The F1 score (also F-score or F-measure) isa measure of a test's accuracy. It considers both the precision and therecall of the test to compute the score:F1=2×((precision×recall)/(precision+recall)) and is the harmonic mean ofprecision and recall. The F1 score reaches its best value at 1 (perfectprecision and recall) and worst at 0.

Autonomous Agents or Chatbots

A conversation between Human A and Human B is a form of discourse. Forexample, applications exist such as FaceBook® Messenger, WhatsApp®,Slack,® SMS, etc., a conversation between A and B may typically be viamessages in addition to more traditional email and voice conversations.A chatbot (which may also be called intelligent bots or virtualassistant, etc.) is an “intelligent” machine that, for example, replaceshuman B and to various degrees mimics the conversation between twohumans. An example ultimate goal is that human A cannot tell whether Bis a human or a machine (the Turning test, developed by Alan Turing in1950). Discourse analysis, artificial intelligence, including machinelearning, and natural language processing, have made great stridestoward the long-term goal of passing the Turing test. Of course, withcomputers being more and more capable of searching and processing vastrepositories of data and performing complex analysis on the data toinclude predictive analysis, the long-term goal is the chatbot beinghuman-like and a computer combined.

For example, users can interact with the Intelligent Bots Platformthrough a conversational interaction. This interaction, also called theconversational user interface (UI), is a dialog between the end user andthe chatbot, just as between two human beings. It could be as simple asthe end user saying “Hello” to the chatbot and the chatbot respondingwith a “Hi” and asking the user how it can help, or it could be atransactional interaction in a banking chatbot, such as transferringmoney from one account to the other, or an informational interaction ina HR chatbot, such as checking for vacation balance, or asking an FAQ ina retail chatbot, such as how to handle returns. Natural languageprocessing (NLP) and machine learning (ML) algorithms combined withother approaches can be used to classify end user intent. An intent at ahigh level is what the end user would like to accomplish (e.g., getaccount balance, make a purchase). An intent is essentially, a mappingof customer input to a unit of work that the backend should perform.Therefore, based on the phrases uttered by the user in the chatbot,these are mapped that to a specific and discrete use case or unit ofwork, for e.g. check balance, transfer money and track spending are all“use cases” that the chatbot should support and be able to work outwhich unit of work should be triggered from the free text entry that theend user types in a natural language.

The underlying rational for having an AI chatbot respond like a human isthat the human brain can formulate and understand the request and thengive a good response to the human request much better than a machine.Thus, there should be significant improvement in the request/response ofa chatbot, if human B is mimicked. So an initial part of the problem ishow does the human brain formulate and understand the request? To mimic,a model is used. RST and

DT allow a formal and repeatable way of doing this.

At a high level, there are typically two types of requests: (1) Arequest to perform some action; and (2) a request for information, e.g.,a question. The first type has a response in which a unit of work iscreated. The second type has a response that is, e.g., a good answer, tothe question. The answer could take the form of, for example, in someaspects, the AI constructing an answer from its extensive knowledgebase(s) or from matching the best existing answer from searching theinternet or intranet or other publically/privately available datasources.

Communicative Discourse Trees and the Rhetoric Classifier

Aspects of the present disclosure build communicative discourse treesand use communicative discourse trees to analyze whether the rhetoricalstructure of a request or question agrees with an answer. Morespecifically, aspects described herein create representations of arequest-response pair, learns the representations, and relates the pairsinto classes of valid or invalid pairs. In this manner, an autonomousagent can receive a question from a user, process the question, forexample, by searching for multiple answers, determine the best answerfrom the answers, and provide the answer to the user.

More specifically, to represent linguistic features of text, aspectsdescribed herein use rhetoric relations and speech acts (orcommunicative actions). Rhetoric relations are relationships between theparts of the sentences, typically obtained from a discourse tree. Speechacts are obtained as verbs from a verb resource such as VerbNet. Byusing both rhetoric relations and communicative actions, aspectsdescribed herein can correctly recognize valid request-response pairs.To do so, aspects correlate the syntactic structure of a question withthat of an answer. By using the structure, a better answer can bedetermined.

For example, when an autonomous agent receives an indication from aperson that the person desires to sell an item with certain features,the autonomous agent should provide a search result that not onlycontains the features but also indicates an intent to buy. In thismanner, the autonomous agent has determined the user's intent.Similarly, when an autonomous agent receives a request from a person toshare knowledge about a particular item, the search result shouldcontain an intent to receive a recommendation. When a person asks anautonomous agent for an opinion about a subject, the autonomous agentshares an opinion about the subject, rather than soliciting anotheropinion.

Analyzing Request and Response Pairs

FIG. 7 depicts an exemplary DT for an example request about property taxin accordance with an aspect. The node labels are the relations and thearrowed line points to the satellite. The nucleus is a solid line. FIG.7 depicts the following text.

Request: “My husbands' grandmother gave him his grandfather's truck. Shesigned the title over but due to my husband having unpaid fines on hislicense, he was not able to get the truck put in his name. I wanted toput in my name and paid the property tax and got insurance for thetruck. By the time it came to sending off the title and getting the tag,I didn't have the money to do so. Now, due to circumstances, I am notgoing to be able to afford the truck. I went to the insurance place andwas refused a refund. I am just wondering that since I am not going tohave a tag on this truck, is it possible to get the property taxrefunded?”

Response: “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax. If you apply late, therewill be penalties on top of the normal taxes and fees. You don't need toregister it at the same time, but you absolutely need to title it withinthe period of time stipulated in state law.”

As can be seen in FIG. 7, analyzing the above text results in thefollowing. “My husbands' grandmother gave him his grandfather's truck”is elaborated by “She signed the title over but due to my husband”elaborated by “having unpaid fines on his license, he was not able toget the truck put in his name.” which is elaborated by “I wanted to putin my name,” “and paid the property tax”, and “and got insurance for thetruck.”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck.” iselaborated by;

“I didn't have the money” elaborated by “to do so” contrasted with

“By the time” elaborated by “it came to sending off the title”

“and getting the tag”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so” is contrasted with

“Now, due to circumstances,” elaborated with “I am not going to be ableto afford the truck.” which is elaborated with

“I went to the insurance place”

“and was refused a refund”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so. Now, due to circumstances, I am not going to be ableto afford the truck. I went to the insurance place and was refused arefund.” is elaborated with

“I am just wondering that since I am not going to have a tag on thistruck, is it possible to get the property tax refunded?”

“I am just wondering” has attribution to

“that” is the same unit as “is it possible to get the property taxrefunded?” which has condition “since I am not going to have a tag onthis truck”

As can be seen, the main subject of the topic is “Property tax on acar”. The question includes the contradiction: on one hand, allproperties are taxable, and on the other hand, the ownership is somewhatincomplete. A good response has to address both topic of the questionand clarify the inconsistency. To do that, the responder is making evenstronger claim concerning the necessity to pay tax on whatever is ownedirrespectively of the registration status. This example is a member ofpositive training set from our Yahoo! Answers evaluation domain. Themain subject of the topic is “Property tax on a car”. The questionincludes the contradiction: on one hand, all properties are taxable, andon the other hand, the ownership is somewhat incomplete. A goodanswer/response has to address both topic of the question and clarifythe inconsistency. The reader can observe that since the questionincludes rhetoric relation of contrast, the answer has to match it witha similar relation to be convincing. Otherwise, this answer would lookincomplete even to those who are not domain experts.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7, according to certain aspects of the present invention. Thecentral nucleus is “the property tax is assessed on property” elaboratedby “that you own”. “The property tax is assessed on property that youown” is also a nucleus elaborated by “Just because you chose to notregister it does not mean that you don't own it, so the tax is notrefundable. Even if you have not titled the vehicle yet, you still ownit within the boundaries of the tax district, so the tax is payable.Note that all states give you a limited amount of time to transfer titleand pay the use tax.”

The nucleus “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax.” is elaborated by “therewill be penalties on top of the normal taxes and fees” with condition“If you apply late,” which in turn is elaborated by the contrast of “butyou absolutely need to title it within the period of time stipulated instate law.” and “You don't need to register it at the same time.”.

Comparing the DT of FIG. 7 and DT of FIG. 8, enables a determination ofhow well matched the response (FIG. 8) is to the request (FIG. 7). Insome aspects of the present invention, the above framework is used, atleast in part, to determine the DTs for the request/response and therhetoric agreement between the DTs.

In another example, the question “What does The Investigative Committeeof the Russian Federation do” has at least two answers, for example, anofficial answer or an actual answer.

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect. As depicted in FIG. 9, an official answer, or missionstatement states that “The Investigative Committee of the RussianFederation is the main federal investigating authority which operates asRussia's Anti-corruption agency and has statutory responsibility forinspecting the police forces, combating police corruption and policemisconduct, is responsible for conducting investigations into localauthorities and federal governmental bodies.”

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect. As depicted in FIG. 10, another, perhaps more honest, answerstates that “Investigative Committee of the Russian Federation issupposed to fight corruption. However, top-rank officers of theInvestigative Committee of the Russian Federation are charged withcreation of a criminal community. Not only that, but their involvementin large bribes, money laundering, obstruction of justice, abuse ofpower, extortion, and racketeering has been reported. Due to theactivities of these officers, dozens of high-profile cases including theones against criminal lords had been ultimately ruined.”

The choice of answers depends on context. Rhetoric structure allowsdifferentiating between “official”, “politically correct”,template-based answers and “actual”, “raw”, “reports from the field”, or“controversial” answers, see FIGS. 9 and 10). Sometimes, the questionitself can give a hint about which category of answers is expected. If aquestion is formulated as a factoid or definitional one, without asecond meaning, then the first category of answers is suitable.Otherwise, if a question has the meaning “tell me what it really is”,then the second category is appropriate. In general, after extracting arhetoric structure from a question, selecting a suitable answer thatwould have a similar, matching, or complementary rhetoric structure iseasier.

The official answer is based on elaboration and joints, which areneutral in terms of controversy a text might contain (See FIG. 9). Atthe same time, the row answer includes the contrast relation. Thisrelation is extracted between the phrase for what an agent is expectedto do and what this agent was discovered to have done.

Classification of Request-Response Pairs

Classification application 102 can determine whether a given answer orresponse, such as an answer obtained from answer database 105 or apublic database, is responsive to a given question, or request. Morespecifically, classification application 102 analyzes whether a requestand response pair is correct or incorrect by determining one or both of(i) relevance or (ii) rhetoric agreement between the request and theresponse. Rhetoric agreement can be analyzed without taking into accountrelevance, which can be treated orthogonally.

Classification application 102 can determine similarity betweenquestion-answer pairs using different methods. For example,classification application 102 can determine level of similarity betweenan individual question and an individual answer. Alternatively,classification application 102 can determine a measure of similaritybetween a first pair including a question and an answer, and a secondpair including a question and answer.

For example, classification application 102 uses classifier 120 trainedto predict matching or non-matching answers. Classification application102 can process two pairs at a time, for example <q1, a1> and <q2,a2>.Classification application 102 compares q1 with q2 and a1 with a1,producing a combined similarity score. Such a comparison allows adetermination of whether an unknown question/answer pair contains acorrect answer or not by assessing a distance from anotherquestion/answer pair with a known label. In particular, an unlabeledpair <q2, a2> can be processed so that rather than “guessing”correctness based on words or structures shared by q2 and a2, both q2and a2 can be compared with their corresponding components q1 and a2 ofthe labeled pair <q2, a2> on the grounds of such words or structures.Because this approach targets a domain-independent classification of ananswer, only the structural cohesiveness between a question and answercan be leveraged, not ‘meanings’ of answers.

In an aspect, classification application 102 uses training data 125 totrain classifier 120. In this manner, classifier 120 is trained todetermine a similarity between pairs of questions and answers. This is aclassification problem. Training data 125 can include a positivetraining set and a negative training set. Training data 125 includesmatching request-response pairs in a positive dataset and arbitrary orlower relevance or appropriateness request-response pairs in a negativedataset. For the positive dataset, various domains with distinctacceptance criteria are selected that indicate whether an answer orresponse is suitable for the question.

Each training data set includes a set of training pairs. Each trainingset includes a question communicative discourse tree that represents aquestion and an answer communicative discourse tree that represents ananswer and an expected level of complementarity between the question andanswer. By using an iterative process, classification application 102provides a training pair to classifier 120 and receives, from the model,a level of complementarity. Classification application 102 calculates aloss function by determining a difference between the determined levelof complementarity and an expected level of complementarity for theparticular training pair. Based on the loss function, classificationapplication 102 adjusts internal parameters of the classification modelto minimize the loss function.

Acceptance criteria can vary by application. For example, acceptancecriteria may be low for community question answering, automated questionanswering, automated and manual customer support systems, social networkcommunications and writing by individuals such as consumers about theirexperience with products, such as reviews and complaints. RR acceptancecriteria may be high in scientific texts, professional journalism,health and legal documents in the form of FAQ, professional socialnetworks such as “stackoverflow.”

Communicative Discourse Trees (CDTs)

Classification application 102 can create, analyze, and comparecommunicative discourse trees. Communicative discourse trees aredesigned to combine rhetoric information with speech act structures.CDTs include with arcs labeled with expressions for communicativeactions. By combining communicative actions, CDTs enable the modeling ofRST relations and communicative actions. A CDT is a reduction of a parsethicket. See Galitsky, B, Ilvovsky, D. and Kuznetsov S O. Rhetoric Mapof an Answer to Compound Queries Knowledge Trail Inc. ACL 2015, 681-686.(“Galitsky 2015”). A parse thicket is a combination of parse trees forsentences with discourse-level relationships between words and parts ofthe sentence in one graph. By incorporating labels that identify speechactions, learning of communicative discourse trees can occur over aricher features set than just rhetoric relations and syntax ofelementary discourse units (EDUs).

In an example, a dispute between three parties concerning the causes ofa downing of a commercial airliner, Malaysia Airlines Flight 17 isanalyzed. An RST representation of the arguments being communicated isbuilt. In the example, three conflicting agents, Dutch investigators,The Investigative Committee of the Russian Federation, and theself-proclaimed Donetsk People's Republic exchange their opinions on thematter. The example illustrates a controversial conflict where eachparty does all it can to blame its opponent. To sound more convincing,each party does not just produce its claim but formulates a response ina way to rebuff the claims of an opponent. To achieve this goal, eachparty attempts to match the style and discourse of the opponents'claims.

FIG. 11 illustrates a communicative discourse tree for a claim of afirst agent in accordance with an aspect. FIG. 11 depicts communicativediscourse tree 100, which represents the following text: “Dutch accidentinvestigators say that evidence points to pro-Russian rebels as beingresponsible for shooting down plane. The report indicates where themissile was fired from and identifies who was in control of theterritory and pins the downing of MH17 on the pro-Russian rebels.”

As can be seen from FIG. 11, non-terminal nodes of CDTs are rhetoricrelations, and terminal nodes are elementary discourse units (phrases,sentence fragments) which are the subjects of these relations. Certainarcs of CDTs are labeled with the expressions for communicative actions,including the actor agent and the subject of these actions (what isbeing communicated). For example, the nucleus node for elaborationrelation (on the left) are labeled with say (Dutch, evidence), and thesatellite with responsible(rebels, shooting down). These labels are notintended to express that the subjects of EDUs are evidence and shootingdown but instead for matching this CDT with others for the purpose offinding similarity between them. In this case just linking thesecommunicative actions by a rhetoric relation and not providinginformation of communicative discourse would be too limited way torepresent a structure of what and how is being communicated. Arequirement for an RR pair to have the same or coordinated rhetoricrelation is too weak, so an agreement of CDT labels for arcs on top ofmatching nodes is required.

The straight edges of this graph are syntactic relations, and curvy arcsare discourse relations, such as anaphora, same entity, sub-entity,rhetoric relation and communicative actions. This graph includes muchricher information than just a combination of parse trees for individualsentences. In addition to CDTs, parse thickets can be generalized at thelevel of words, relations, phrases and sentences. The speech actions arelogic predicates expressing the agents involved in the respective speechacts and their subjects. The arguments of logical predicates are formedin accordance to respective semantic roles, as proposed by a frameworksuch as VerbNet. See Karin Kipper, Anna Korhonen, Neville Ryant, MarthaPalmer, A Large-scale Classification of English Verbs, LanguageResources and Evaluation Journal, 42(1), pp. 21-40, Springer Netherland,2008. and/or Karin Kipper Schuler, Anna Korhonen, Susan W. Brown,VerbNet overview, extensions, mappings and apps, Tutorial, NAACL-HLT2009, Boulder, Colo.

FIG. 12 illustrates a communicative discourse tree for a claim of asecond agent in accordance with an aspect. FIG. 12 depicts communicativediscourse tree 1200, which represents the following text: “TheInvestigative Committee of the Russian Federation believes that theplane was hit by a missile, which was not produced in Russia. Thecommittee cites an investigation that established the type of themissile.”

FIG. 13 illustrates a communicative discourse tree for a claim of athird agent in accordance with an aspect. FIG. 13 depicts communicativediscourse tree 1300, which represents the following text: “Rebels, theself-proclaimed Donetsk People's Republic, deny that they controlled theterritory from which the missile was allegedly fired. It became possibleonly after three months after the tragedy to say if rebels controlledone or another town.”

As can be seen from communicative discourse trees 1100-1300, a responseis not arbitrary. A response talks about the same entities as theoriginal text. For example, communicative discourse trees 1200 and 1300are related to communicative discourse tree 1100. A response backs up adisagreement with estimates and sentiments about these entities, andabout actions of these entities.

More specifically, replies of involved agent need to reflect thecommunicative discourse of the first, seed message. As a simpleobservation, because the first agent uses Attribution to communicate hisclaims, the other agents have to follow the suite and either providetheir own attributions or attack the validity of attribution of theproponent, or both. To capture a broad variety of features for howcommunicative structure of the seed message needs to be retained inconsecutive messages, pairs of respective CDTs can be learned.

To verify the agreement of a request-response, discourse relations orspeech acts (communicative actions) alone are often insufficient. As canbe seen from the example depicted in FIGS. 11-13, the discoursestructure of interactions between agents and the kind of interactionsare useful. However, the domain of interaction (e.g., military conflictsor politics) or the subjects of these interactions, i.e., the entities,do not need to be analyzed.

Representing Rhetoric Relations and Communicative Actions

In order to compute similarity between abstract structures, twoapproaches are frequently used: (1) representing these structures in anumerical space, and express similarity as a number, which is astatistical learning approach, or (2) using a structural representation,without numerical space, such as trees and graphs, and expressingsimilarity as a maximal common sub-structure. Expressing similarity as amaximal common sub-structure is referred to as generalization.

Learning communicative actions helps express and understand arguments.Computational verb lexicons help support acquisition of entities foractions and provide a rule-based form to express their meanings. Verbsexpress the semantics of an event being described as well as therelational information among participants in that event, and project thesyntactic structures that encode that information. Verbs, and inparticular communicative action verbs, can be highly variable and candisplay a rich range of semantic behaviors. In response, verbclassification helps a learning systems to deal with this complexity byorganizing verbs into groups that share core semantic properties.

VerbNet is one such lexicon, which identifies semantic roles andsyntactic patterns characteristic of the verbs in each class and makesexplicit the connections between the syntactic patterns and theunderlying semantic relations that can be inferred for all members ofthe class. See Karin Kipper, Anna Korhonen, Neville Ryant and MarthaPalmer, Language Resources and Evaluation, Vol. 42, No. 1 (March 2008),at 21. Each syntactic frame, or verb signature, for a class has acorresponding semantic representation that details the semanticrelations between event participants across the course of the event.

For example, the verb amuse is part of a cluster of similar verbs thathave a similar structure of arguments (semantic roles) such as amaze,anger, arouse, disturb, and irritate. The roles of the arguments ofthese communicative actions are as follows: Experiencer (usually, ananimate entity), Stimulus, and Result. Each verb can have classes ofmeanings differentiated by syntactic features for how this verb occursin a sentence, or frames. For example, the frames for amuse are asfollows, using the following key noun phrase (NP), noun (N),communicative action (V), verb phrase (VP), adverb (ADV):

NP V NP. Example: “The teacher amused the children.” Syntax: Stimulus VExperiencer. Clause: amuse(Stimulus, E, Emotion, Experiencer),cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer).

NP V ADV-Middle. Example: “Small children amuse quickly.” Syntax:Experiencer V ADV. Clause: amuse(Experiencer, Prop):—,property(Experiencer, Prop), adv(Prop).

NP V NP-PRO-ARB. Example “The teacher amused.” Syntax Stimulus V.amuse(Stimulus, E, Emotion, Experiencer): cause(Stimulus, E),emotional_state(result(E), Emotion, Experiencer).

NP.cause V NP. Example “The teacher's dolls amused the children.” syntaxStimulus <+genitive>('s) V Experiencer. amuse(Stimulus, E, Emotion,Experiencer): cause(Stimulus, E), emotional_state(during(E), Emotion,Experiencer).

NP V NP ADJ. Example “This performance bored me totally.” syntaxStimulus V Experiencer Result. amuse(Stimulus, E, Emotion, Experiencer).cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer),Pred(result(E), Experiencer).

Communicative actions can be characterized into clusters, for example:

Verbs with Predicative Complements (Appoint, characterize, dub, declare,conjecture, masquerade, orphan, captain, consider, classify), Verbs ofPerception (See, sight, peer).Verbs of Psychological State (Amuse, admire, marvel, appeal), Verbs ofDesire (Want, long).Judgment Verbs (Judgment), Verbs of Assessment (Assess, estimate), Verbsof Searching (Hunt, search, stalk, investigate, rummage, ferret), Verbsof Social Interaction (Correspond, marry, meet, battle), Verbs ofCommunication (Transfer(message), inquire, interrogate, tell,manner(speaking), talk, chat, say, complain, advise, confess, lecture,overstate, promise). Avoid Verbs (Avoid), Measure Verbs, (Register,cost, fit, price, bill), Aspectual Verbs (Begin, complete, continue,stop, establish, sustain.

Aspects described herein provide advantages over statistical learningmodels. In contrast to statistical solutions, aspects use aclassification system can provide a verb or a verb-like structure whichis determined to cause the target feature (such as rhetoric agreement).For example, statistical machine learning models express similarity as anumber, which can make interpretation difficult.

Representing Request-Response Pairs

Representing request-response pairs facilitates classification basedoperations based on a pair. In an example, request-response pairs can berepresented as parse thickets. A parse thicket is a representation ofparse trees for two or more sentences with discourse-level relationshipsbetween words and parts of the sentence in one graph. See Galitsky 2015.Topical similarity between question and answer can expressed as commonsub-graphs of parse thickets. The higher the number of common graphnodes, the higher the similarity.

FIG. 14 illustrates parse thickets in accordance with an aspect. FIG. 14depicts parse thicket 1400 including a parse tree 1401 for a request,and a parse tree for a corresponding response 1402.

Parse tree 1401 represents the question “I just had a baby and it looksmore like the husband I had my baby with. However it does not look likeme at all and I am scared that he was cheating on me with another ladyand I had her kid. This child is the best thing that has ever happenedto me and I cannot imagine giving my baby to the real mom.”

Response 1402 represents the response “Marital therapists advise ondealing with a child being born from an affair as follows. One option isfor the husband to avoid contact but just have the basic legal andfinancial commitments. Another option is to have the wife fully involvedand have the baby fully integrated into the family just like a childfrom a previous marriage.”

FIG. 14 represents a greedy approach to representing linguisticinformation about a paragraph of text. The straight edges of this graphare syntactic relations, and curvy arcs are discourse relations, such asanaphora, same entity, sub-entity, rhetoric relation and communicativeactions. The solid arcs are for same entity/sub-entity/anaphorarelations, and the dotted arcs are for rhetoric relations andcommunicative actions. Oval labels in straight edges denote thesyntactic relations. Lemmas are written in the boxes for the nodes, andlemma forms are written on the right side of the nodes.

Parse thicket 1400 includes much richer information than just acombination of parse trees for individual sentences. Navigation throughthis graph along the edges for syntactic relations as well as arcs fordiscourse relations allows to transform a given parse thicket intosemantically equivalent forms for matching with other parse thickets,performing a text similarity assessment task. To form a complete formalrepresentation of a paragraph, as many links as possible are expressed.Each of the discourse arcs produces a pair of thicket phrases that canbe a potential match.

Topical similarity between the seed (request) and response is expressedas common sub-graphs of parse thickets. They are visualized as connectedclouds. The higher the number of common graph nodes, the higher thesimilarity. For rhetoric agreement, common sub-graph does not have to belarge as it is in the given text. However, rhetoric relations andcommunicative actions of the seed and response are correlated and acorrespondence is required.

Generalization for Communicative Actions

A similarity between two communicative actions A₁ and A₂ is defined as aan abstract verb which possesses the features which are common betweenA₁ and A₂. Defining a similarity of two verbs as an abstract verb-likestructure supports inductive learning tasks, such as a rhetoricagreement assessment. In an example, a similarity between the followingtwo common verbs, agree and disagree, can be generalized as follows:agree{circumflex over ( )}disagree=verb(Interlocutor, Proposed_action,Speaker), where Interlocution is the person who proposed theProposed_action to the Speaker and to whom the Speaker communicatestheir response. Proposed_action is an action that the Speaker wouldperform if they were to accept or refuse the request or offer, and TheSpeaker is the person to whom a particular action has been proposed andwho responds to the request or offer made.

In a further example, a similarity between verbs agree and explain isrepresented as follows: agree{circumflex over( )}explain=verb(Interlocutor, *, Speaker). The subjects ofcommunicative actions are generalized in the context of communicativeactions and are not be generalized with other “physical” actions. Hence,aspects generalize individual occurrences of communicative actionstogether with corresponding subjects.

Additionally, sequences of communicative actions representing dialogscan be compared against other such sequences of similar dialogs. In thismanner, the meaning of an individual communicative action as well as thedynamic discourse structure of a dialogue is (in contrast to its staticstructure reflected via rhetoric relations) is represented. Ageneralization is a compound structural representation that happens ateach level. Lemma of a communicative action is generalized with lemma,and its semantic role are generalized with respective semantic role.

Communicative actions are used by text authors to indicate a structureof a dialogue or a conflict. See Searle, J. R. 1969, Speech acts: anessay in the philosophy of language. London: Cambridge University Press.Subjects are generalized in the context of these actions and are notgeneralized with other “physical” actions. Hence, the individualoccurrences of communicative actions together are generalized with theirsubjects, as well as their pairs, as discourse “steps.”

Generalization of communicative actions can also be thought of from thestandpoint of matching the verb frames, such as VerbNet. Thecommunicative links reflect the discourse structure associated withparticipation (or mentioning) of more than a single agent in the text.The links form a sequence connecting the words for communicative actions(either verbs or multi-words implicitly indicating a communicativeintent of a person).

Communicative actions include an actor, one or more agents being actedupon, and the phrase describing the features of this action. Acommunicative action can be described as a function of the form: verb(agent, subject, cause), where verb characterizes some type ofinteraction between involved agents (e.g., explain, confirm, remind,disagree, deny, etc.), subject refers to the information transmitted orobject described, and cause refers to the motivation or explanation forthe subject.

A scenario (labeled directed graph) is a sub-graph of a parse thicketG=(V, A), where V={action₁, action₂ . . . action_(n)} is a finite set ofvertices corresponding to communicative actions, and A is a finite setof labeled arcs (ordered pairs of vertices), classified as follows:

Each arc action_(i), action_(j)∈A_(sequence) corresponds to a temporalprecedence of two actions v_(i), ag_(i), s_(i), c_(i) and v_(j), ag_(j),s_(j), c_(j) that refer to the same subject, e.g., s_(j)=s_(i) ordifferent subjects. Each arc action_(i), action_(j)∈A_(cause)corresponds to an attack relationship between action_(i) and action_(j)indicating that the cause of action_(i) in conflict with the subject orcause of action_(j).

Subgraphs of parse thickets associated with scenarios of interactionbetween agents have some distinguishing features. For example, (1) allvertices are ordered in time, so that there is one incoming arc and oneoutgoing arc for all vertices (except the initial and terminalvertices), (2) for A_(sequence) arcs, at most one incoming and only oneoutgoing arc are admissible, and (3) for A_(cause) arcs, there can bemany outgoing arcs from a given vertex, as well as many incoming arcs.The vertices involved may be associated with different agents or withthe same agent (i.e., when he contradicts himself). To computesimilarities between parse thickets and their communicative action,induced subgraphs, the sub-graphs of the same configuration with similarlabels of arcs and strict correspondence of vertices are analyzed.

The following similarities exist by analyzing the arcs of thecommunicative actions of a parse thicket: (1) one communicative actionfrom with its subject from T1 against another communicative action withits subject from T2 (communicative action arc is not used), and (2) apair of communicative actions with their subjects from T1 compared toanother pair of communicative actions from T2 (communicative action arcsare used).

Generalizing two different communicative actions is based on theirattributes. See (Galitsky et al 2013). As can be seen in the examplediscussed with respect to FIG. 14, one communicative action from T1,cheating(husband, wife, another lady) can be compared with a second fromT2, avoid(husband, contact(husband, another lady)). A generalizationresults in communicative_action(husband, *) which introduces aconstraint on A in the form that if a given agent (=husband) ismentioned as a subject of CA in Q, he(she) should also be a subject of(possibly, another) CA in A. Two communicative actions can always begeneralized, which is not the case for their subjects: if theirgeneralization result is empty, the generalization result ofcommunicative actions with these subjects is also empty.

Generalization of RST Relations

Some relations between discourse trees can be generalized, such as arcsthat represent the same type of relation (presentation relation, such asantithesis, subject matter relation, such as condition, and multinuclearrelation, such as list) can be generalized. A nucleus or a situationpresented by a nucleus is indicated by “N.” Satellite or situationspresented by a satellite, are indicated by “S.” “W” indicates a writer.“R” indicates a reader (hearer). Situations are propositions, completedactions or actions in progress, and communicative actions and states(including beliefs, desires, approve, explain, reconcile and others).Generalization of two RST relations with the above parameters isexpressed as: rst1(N1, S1, W1, R1){circumflex over ( )}rst2(N2, S2, W2,R2)=(rst1 {circumflex over ( )}rst2)(N1{circumflex over ( )}N2,S1{circumflex over ( )}S2, W1{circumflex over ( )}W2, R1{circumflex over( )}R2).

The texts in N1, S1, W1, R1 are subject to generalization as phrases.For example, rst1 {circumflex over ( )}rst2 can be generalized asfollows: (1) if relation_type(rst1)!=relation_type(rst2) then ageneralization is empty. (2) Otherwise, the signatures of rhetoricrelations are generalized as sentences: sentence(N1, S1, W1, R1)sentence(N2, S2, W2, R2). See Iruskieta, Mikel, Iria da Cunha and MaiteTaboada. A qualitative comparison method for rhetorical structures:identifying different discourse structures in multilingual corpora. LangResources & Evaluation. June 2015, Volume 49, Issue 2.

For example, the meaning of rst−background{circumflex over( )}rst−enablement=(S increases the ability of R to comprehend anelement in N){circumflex over ( )}(R comprehending S increases theability of R to perform the action in N)=increase-VB the-DT ability-NNof-IN R-NN to-IN.

Because the relations rst−background{circumflex over ( )}rst−enablementdiffer, the RST relation part is empty. The expressions that are theverbal definitions of respective RST relations are then generalized. Forexample, for each word or a placeholder for a word such as an agent,this word (with its POS) is retained if the word the same in each inputphrase or remove the word if the word is different between thesephrases. The resultant expression can be interpreted as a common meaningbetween the definitions of two different RST relations, obtainedformally.

Two arcs between the question and the answer depicted in FIG. 14 showthe generalization instance based on the RST relation “RST-contrast”.For example, “I just had a baby” is a RST-contrast with “it does notlook like me,” and related to “husband to avoid contact” which is aRST-contrast with “have the basic legal and financial commitments.” Ascan be seen, the answer need not have to be similar to the verb phraseof the question but the rhetoric structure of the question and answerare similar. Not all phrases in the answer must match phrases inquestion. For example, the phrases that do not match have certainrhetoric relations with the phrases in the answer which are relevant tophrases in question.

Building a Communicative Discourse Tree

FIG. 15 illustrates an exemplary process for building a communicativediscourse tree in accordance with an aspect. Classification application102 can implement process 1500. As discussed, communicative discoursetrees enable improved search engine results.

At block 1501, process 1500 involves accessing a sentence comprisingfragments. At least one fragment includes a verb and words and each wordincludes a role of the words within the fragment, and each fragment isan elementary discourse unit. For example, classification application102 accesses a sentence such as “Rebels, the self-proclaimed DonetskPeople's Republic, deny that they controlled the territory from whichthe missile was allegedly fired” as described with respect to FIG. 13.

Continuing the example, classification application 102 determines thatthe sentence includes several fragments. For example, a first fragmentis “rebels . . . deny.” A second fragment is “that they controlled theterritory.” A third fragment is “from which the missile was allegedlyfired.” Each fragment includes a verb, for example, “deny” for the firstfragment and “controlled” for the second fragment. Although, a fragmentneed not include a verb.

At block 1502, process 1500 involves generating a discourse tree thatrepresents rhetorical relationships between the sentence fragments. Thediscourse tree including nodes, each nonterminal node representing arhetorical relationship between two of the sentence fragments and eachterminal node of the nodes of the discourse tree is associated with oneof the sentence fragments.

Continuing the example, classification application 102 generates adiscourse tree as shown in FIG. 13. For example, the third fragment,“from which the missile was allegedly fired” elaborates on “that theycontrolled the territory.” The second and third fragments togetherrelate to attribution of what happened, i.e., the attack cannot havebeen the rebels because they do not control the territory.

At block 1503, process 1500 involves accessing multiple verb signatures.For example, classification application 102 accesses a list of verbs,e.g., from VerbNet. Each verb matches or is related to the verb of thefragment. For example, the for the first fragment, the verb is “deny.”Accordingly, classification application 102 accesses a list of verbsignatures that relate to the verb deny.

As discussed, each verb signature includes the verb of the fragment andone or more of thematic roles. For example, a signature includes one ormore of noun phrase (NP), noun (N), communicative action (V), verbphrase (VP), or adverb (ADV). The thematic roles describing therelationship between the verb and related words. For example “theteacher amused the children” has a different signature from “smallchildren amuse quickly.” For the first fragment, the verb “deny,”classification application 102 accesses a list of frames, or verbsignatures for verbs that match “deny.” The list is “NP V NP to be NP,”“NP V that S” and “NP V NP.”

Each verb signature includes thematic roles. A thematic role refers tothe role of the verb in the sentence fragment. Classificationapplication 102 determines the thematic roles in each verb signature.Example thematic roles include actor, agent, asset, attribute,beneficiary, cause, location destination source, destination, source,location, experiencer, extent, instrument, material and product,material, product, patient, predicate, recipient, stimulus, theme, time,or topic.

At block 1504, process 1500 involves determining, for each verbsignature of the verb signatures, a number of thematic roles of therespective signature that matches a role of a word in the fragment. Forthe first fragment, classification application 102 determines that theverb “deny” has only three roles, “agent”, “verb” and “theme.”

At block 1505, process 1500 involves selecting a particular verbsignature from the verb signatures based on the particular verbsignature having a highest number of matches. For example, referringagain to FIG. 13, deny in the first fragment “the rebels deny . . . thatthey control the territory” is matched to verb signature deny “NP V NP”,and “control” is matched to control (rebel, territory). Verb signaturesare nested, resulting in a nested signature of “deny(rebel,control(rebel, territory)).”

Representing a Request-Response

Request-response pairs can be analyzed alone or as pairs. In an example,request-response pairs can be chained together. In a chain, rhetoricagreement is expected to hold not only between consecutive members butalso triples and four-tuples. A discourse tree can be constructed for atext expressing a sequence of request-response pairs. For example, inthe domain of customer complaints, request and response are present inthe same text, from the viewpoint of a complainant. Customer complainttext can to be split into request and response text portions and thenform the positive and negative dataset of pairs. In an example, all textfor the proponent and all text for the opponent is combined. The firstsentence of each paragraph below will form the Request part (which willinclude three sentences) and second sentence of each paragraph will formthe Response part (which will also include three sentences in thisexample).

FIG. 16 illustrates a discourse tree and scenario graph in accordancewith an aspect. FIG. 16 depicts discourse tree 1601 and scenario graph1602. Discourse tree 1601 corresponds to the following three sentences:

(1) I explained that my check bounced (I wrote it after I made adeposit). A customer service representative accepted that it usuallytakes some time to process the deposit.

(2) I reminded that I was unfairly charged an overdraft fee a month agoin a similar situation. They denied that it was unfair because theoverdraft fee was disclosed in my account information.

(3) I disagreed with their fee and wanted this fee deposited back to myaccount. They explained that nothing can be done at this point and thatI need to look into the account rules closer.

As can be seen by the discourse tree in FIG. 16, determining whether thetext represents an interaction or a description can be hard to judge.Hence, by analyzing the arcs of communicative actions of a parsethicket, implicit similarities between texts can be found. For example,in general terms:

(1) one communicative actions from with its subject from a first treeagainst another communicative action with its subject from a second tree(communicative action arc is not used).

(2) a pair of communicative actions with their subjects from a firsttree against another pair of communicative actions from a second tree(communicative action arcs are used).

For example, in the previous example, the generalization ofcheating(husband, wife, another lady){circumflex over ( )}avoid(husband,contact(husband, another lady)) provides uscommunicative_action(husband, *) which introduces a constraint on A inthe form that if a given agent (=husband) is mentioned as a subject ofCA in Q, he(she) should also be a subject of (possibly, another) CA inA.

To handle meaning of words expressing the subjects of CAs, a word can beapplied to a vector model such as the “word2vector” model. Morespecifically, to compute generalization between the subjects ofcommunicative actions, the following rule can be used: ifsubject1=subject2, subject1{circumflex over ( )}subject2=<subject1,POS(subject1), 1>. Here subject remains and score is 1. Otherwise, ifthe subjects have the same part-of-speech (POS), thensubject1{circumflex over ( )}subject2=<*, POS(subject1),word2vecDistance(subject1{circumflex over ( )}subject2)>. denotes thatlemma is a placeholder, and the score is a word2vec distance betweenthese words. If POS is different, generalization is an empty tuple andmay not be further generalized.

Classification Settings for Request-Response Pairs

In a conventional search, as a baseline, the match between requestresponse pairs can be measured in terms of keyword statistics such asshort for term frequency-inverse document frequency (TF*IDF). To improvesearch relevance, this score is augmented by item popularity, itemlocation or taxonomy-based score (Galitsky 2015). Search can also beformulated as a passage re-ranking problem in machine learningframework. The feature space includes request-response pairs aselements, and a separation hyper-plane splits this feature space intocorrect and incorrect pairs. Hence a search problem can be formulated ina local way, as similarity between Req and Resp, or in a global,learning way, via similarity between request-response pairs.

Other methods are possible for determining a match between request andresponse. In a first example, classification application 102 extractsfeatures for Req and Resp and compares the features as a count,introducing a scoring function such that a score would indicate a class(low score for incorrect pairs, high score for correct ones)

In a second example, classification application 102 comparesrepresentations for Req and Resp against each other, and assigns a scorefor the comparison result. Analogously, the score will indicate a class.

In a third example, classification application 102 builds arepresentation for a pair Req and Resp, <Req, Resp> as elements oftraining set. Classification application 102 then performs learning inthe feature space of all such elements <Req, Resp>.

FIG. 17 illustrates forming a request-response pair in accordance withan aspect. FIG. 17 depicts request-response pair 1701, request tree (orobject) 1702, and response tree 1703. To form a <Req, Resp> object, theclassification application 102 combines the discourse tree for therequest and the discourse tree for the response into a single tree withthe root RR. The classification application 102 then classifies theobjects into correct (with high agreement) and incorrect (with lowagreement) categories.

Nearest Neighbor Graph-Based Classification

Once a CDT is built, in order to identify an argument in text,classification application 102 compute the similarity compared to CDTsfor the positive class and verify that it is lower to the set of CDTsfor its negative class. Similarity between CDT is defined by means ofmaximal common sub-CDTs.

In an example, an ordered set G of CDTs(V,E) with vertex- andedge-labels from the sets (Λ_(ξ), ≤) and (Λ_(E), ≤) is constructed. Alabeled CDT Γ from G is a pair of pairs of the form ((V,1),(E,b)), whereV is a set of vertices, E is a set of edges, 1: V→Λ_(ξ) is a functionassigning labels to vertices, and b: E→Λ_(E) is a function assigninglabels to edges. Isomorphic trees with identical labeling are notdistinguished.

The order is defined as follows: For two CDTs Γ₁:=((V₁,1₁),(E₁,b₁)) andΓ₂:=((V₂,1₂),(E₂,b₂)) from G, then that Γ₁ dominates Γ₂ or Γ₂≤Γ₁ (or Γ₂is a sub-CDT of Γ₁) if there exists a one-to-one mapping φ: V₂→V₁ suchthat it (1) respects edges: (v,w)∈E₂⇒(φ(v), φ(w))∈E₁, and (2) fits underlabels: 1₂(v)≤1₁(φ(v)), (v,w)∈E₂⇒b₂(v,w)≤b₁(φ(v), φ(w)).

This definition takes into account the calculation of similarity(“weakening”) of labels of matched vertices when passing from the“larger” CDT G₁ to “smaller” CDT G₂.

Now, similarity CDT Z of a pair of CDTs X and Y, denoted by X{circumflexover ( )}Y=Z, is the set of all inclusion-maximal common sub-CDTs of Xand Y, each of them satisfying the following additional conditions (1)to be matched, two vertices from CDTs X and Y must denote the same RSTrelation; and (2) each common sub-CDT from Z contains at least onecommunicative action with the same VerbNet signature as in X and Y.

This definition is easily extended to finding generalizations of severalgraphs. The subsumption order μ on pairs of graph sets X and Y isnaturally defined as XμY:=X*Y=X.

FIG. 18 illustrates a maximal common sub-communicative discourse tree inaccordance with an aspect. Notice that the tree is inverted and thelabels of arcs are generalized: Communicative action site( ) isgeneralized with communicative action say( ). The first (agent) argumentof the former CA committee is generalized with the first argument of thelatter CA Dutch. The same operation is applied to the second argumentsfor this pair of CAs: investigator {circumflex over ( )}evidence.

CDT U belongs to a positive class such that (1) U is similar to (has anonempty common sub-CDT) with a positive example R⁺ and (2) for anynegative example R⁻, if U is similar to R⁻ (i.e., U*R⁻≠Ø) thenU*R⁻μU*R⁺.

This condition introduces the measure of similarity and says that to beassigned to a class, the similarity between the unknown CDT U and theclosest CDT from the positive class should be higher than the similaritybetween U and each negative example. Condition 2 implies that there is apositive example R⁺ such that for no R⁻ one has U*R⁺μR⁻, i.e., there isno counterexample to this generalization of positive examples.

Thicket Kernel Learning for CDT

Tree Kernel learning for strings, parse trees and parse thickets is awell-established research area these days. The parse tree kernel countsthe number of common sub-trees as the discourse similarity measurebetween two instances. Tree kernel has been defined for DT by Joty,Shafiq and A. Moschitti. Discriminative Reranking of Discourse ParsesUsing Tree Kernels. Proceedings of EMNLP. (2014). See also Wang, W., Su,J., & Tan, C. L. (2010). Kernel Based Discourse Relation Recognitionwith Temporal Ordering Information. In Proceedings of the 48th AnnualMeeting of the Association for Computational Linguistics. (using thespecial form of tree kernels for discourse relation recognition). Athicket kernel is defined for a CDT by augmenting a DT kernel by theinformation on communicative actions.

A CDT can be represented by a vector V of integer counts of eachsub-tree type (without taking into account its ancestors):

V (T)=(# of subtrees of type 1, . . . , # of subtrees of type I, . . . ,# of subtrees of type n). This results in a very high dimensionalitysince the number of different sub-trees is exponential in its size.Thus, it is computational infeasible to directly use the feature vectorØ(T). To solve the computational issue, a tree kernel function isintroduced to calculate the dot product between the above highdimensional vectors efficiently. Given two tree segments CDT1 and CDT2,the tree kernel function is defined:

K(CDT1,CDT2)=<V(CDT1),V(CDT2)>=Σi V(CDT1)[i],V(CDT2)[i]=Σn1Σn2 ΣiIi(n1)*Ii(n2) where

n1∈N1, n2∈N2 where N1 and N2 are the sets of all nodes in CDT1 and CDT2,respectively;Ii (n) is the indicator function.Ii (n)={1 iff a subtree of type i occurs with root at node; 0otherwise}. K (CDT1, CDT2) is an instance of convolution kernels overtree structures (Collins and Duffy, 2002) and can be computed byrecursive definitions:Δ (n1, n2)=ΣI Ii(n1)*Ii(n2)Δ (n1, n2)=0 if n1 and n2 are assigned the same POS tag or theirchildren are different subtrees.Otherwise, if both n1 and n2 are POS tags (are pre-terminal nodes) thenΔ (n1, n2)=1×λ;Otherwise, Δ (n1, n2)=λΠ_(j=1) ^(nc(n1))(1+Δ(ch(n1, j), ch(n2, j)))where ch(n,j) is the jth child of node n, nc(n₁) is the number of thechildren of n₁, and λ (0<λ<1) is the decay factor in order to make thekernel value less variable with respect to the sub-tree sizes. Inaddition, the recursive rule (3) holds because given two nodes with thesame children, one can construct common sub-trees using these childrenand common sub-trees of further offspring. The parse tree kernel countsthe number of common sub-trees as the syntactic similarity measurebetween two instances.

FIG. 19 illustrates a tree in a kernel learning format for acommunicative discourse tree in accordance with an aspect.

The terms for Communicative Actions as labels are converted into treeswhich are added to respective nodes for RST relations. For texts forEDUs as labels for terminal nodes only the phrase structure is retained.The terminal nodes are labeled with the sequence of phrase types insteadof parse tree fragments.

If there is a rhetoric relation arc from a node X to a terminal EDU nodeY with label A(B, C(D)), then the subtree A -B->(C−D) is appended to X.

Implementation of the Classifier

Classifier 120 can determine the complementarity between two sentences,such as a question and an answer, by using communicative discoursetrees. FIG. 20 illustrates an exemplary process used to implement aclassifier in accordance with an aspect. FIG. 20 depicts process 2000,which can be implemented by classification application 102. Asdiscussed, classifier 120 is trained with training data 125.

Classifier 120 determines a communicative discourse tree for bothquestion and answer. For example, classifier 120 constructs a questioncommunicative discourse tree from a question, and an answercommunicative discourse tree from a candidate answer.

At block 2001, process 2000 involves determining, for a questionsentence, a question communicative discourse tree including a questionroot node. A question sentence can be an explicit question, a request,or a comment. Classification application 102 creates questioncommunicative discourse tree from input text. Using the examplediscussed in relation to FIGS. 13 and 15, an example question sentenceis “are rebels responsible for the downing of the flight.”Classification application 102 can use process 1500 described withrespect to FIG. 15. The example question has a root node of “elaborate.”

At block 2002, process 2000 involves determining, for an answersentence, a second communicative discourse tree, wherein the answercommunicative discourse tree includes an answer root node. Continuingthe above example, classification application 102 creates ancommunicative discourse tree, as depicted in FIG. 13, which also has aroot node “elaborate.”

At block 2003, process 2000 involves associating the communicativediscourse trees by identifying that the question root node and theanswer root node are identical. Classification application 102determines that the question communicative discourse tree and answercommunicative discourse tree have an identical root node. The resultingassociated communicative discourse tree is depicted in FIG. 17 and canbe labeled as a “request-response pair.”

At block 2004, process 2000 involves computing a level ofcomplementarity between the question communicative discourse tree andthe answer communicative discourse tree by applying a predictive modelto the merged discourse tree.

The classifier uses machine learning techniques. In an aspect, theclassification application 102 trains and uses classifier 120. Forexample, classification application 102 defines positive and negativeclasses of request-response pairs. The positive class includesrhetorically correct request-response pairs and the negative classincludes relevant but rhetorically foreign request-response pairs.

For each request-response pair, the classification application 102builds a CDT by parsing each sentence and obtaining verb signatures forthe sentence fragments.

Classification application 102 provides the associated communicativediscourse tree pair to classifier 120. Classifier 120 outputs a level ofcomplementarity.

At block 2005, process 2000 involves responsive to determining that thelevel of complementarity is above a threshold, identifying the questionand answer sentences as complementary. Classification application 102can use a threshold level of complementarity to determine whether thequestion-answer pair is sufficiently complementary. For example, if aclassification score is greater than a threshold, then classificationapplication 102 can output the answer. Alternatively, classificationapplication 102 can discard the answer and access answer database 105 ora public database for another candidate answer and repeat process 2000as necessary.

In an aspect, the classification application 102 obtains co-references.In a further aspect, the classification application 102 obtains entityand sub-entity, or hyponym links. A hyponym is a word of more specificmeaning than a general or superordinate term applicable to the word. Forexample, “spoon” is a hyponym of “cutlery.”

In another aspect, classification application 102 applies thicket kernellearning to the representations. Thicket kernel learning can take placein place of classification-based learning described above, e.g., atblock 2004. The classification application 102 builds a parse thicketpair for the parse tree of the request-response pair. The classificationapplication 102 applies discourse parsing to obtain a discourse treepair for the request-response pair. The classification application 102aligns elementary discourse units of the discourse tree request-responseand the parse tree request-response. The classification application 102merges the elementary discourse units of the discourse treerequest-response and the parse tree request-response.

In an aspect, classification application 102 improves the textsimilarity assessment by word2vector model.

In a further aspect, classification application 102 sends a sentencethat corresponds to the question communicative discourse tree or asentence that corresponds to the answer communicative discourse tree toa device such as mobile device 170. Outputs from classificationapplication 102 can be used as inputs to search queries, databaselookups, or other systems. In this manner, classification application102 can integrate with a search engine system.

FIG. 21 illustrates a chat bot commenting on a posting in accordancewith an aspect. FIG. 21 depicts chat 2100, user messages 2101-2104, andagent response 2105. Agent response 2105 can be implemented by theclassification application 102. As shown, agent response 2105 hasidentified a suitable answer to the thread of user messages 2101-2104.

FIG. 22 illustrates a chat bot commenting on a posting in accordancewith an aspect. FIG. 22 depicts chat 2200, user messages 2201-2205, andagent response 2206. FIG. 22 depicts three messages from user 1,specifically 2201, 2203, and 2205, and two messages from user 2,specifically 2202 and 2204. Agent response 2206 can be implemented bythe classification application 102. As shown, agent response 2106 hasidentified a suitable answer to the thread of messages 2201-2204.

The features depicted in FIGS. 21 and 22 can be implemented by computingdevice 101, or by a device that provides input text to computing device101 and receives an answer from computing device 101.

Additional Rules for RR Agreement and RR Irrationality

The following are the examples of structural rules which introduceconstraint to enforce RR agreement:

1. Both Req and Resp have the same sentiment polarity (If a request ispositive the response should be positive as well, and other way around.2. Both Req and Resp have a logical argument.

Under rational reasoning, Request and Response will fully agree: arational agent will provide an answer which will be both relevant andmatch the question rhetoric. However, in the real world not allresponses are fully rational. The body of research on Cognitive biasesexplores human tendencies to think in certain ways that can lead tosystematic deviations from a standard of rationality or good judgment.

The correspondence bias is the tendency for people to over-emphasizepersonality-based explanations for behaviors observed in others,responding to questions. See Baumeister, R. F. & Bushman, B. J. Socialpsychology and human nature: International Edition. (2010). At the sametime, those responding queries under-emphasize the role and power ofsituational influences on the same behavior.

Confirmation bias, the inclination to search for or interpretinformation in a way that confirms the preconceptions of those answeringquestions. They may discredit information that does not support theirviews. The confirmation bias is related to the concept of cognitivedissonance. Whereby, individuals may reduce inconsistency by searchingfor information which re-confirms their views.

Anchoring leads to relying too heavily, or “anchor”, on one trait orpiece of information when making decisions.

Availability heuristic makes us overestimate the likelihood of eventswith greater “availability” in memory, which can be influenced by howrecent the memories are or how unusual or emotionally charged they maybe.

According to Bandwagon effect, people answer questions believing inthings because many other people do (or believe) the same.

Belief bias is an effect where someone's evaluation of the logicalstrength of an argument is biased by the believability of theconclusion.

Bias blind spot is the tendency to see oneself as less biased than otherpeople, or to be able to identify more cognitive biases in others thanin oneself.

Evaluation

A first domain of test data is derived from question-answer pairs fromYahoo! Answers, which is a set of question-answer pairs with broadtopics. Out of the set of 4.4 million user questions, 20000 are selectedthat each include more than two sentences. Answers for most questionsare fairly detailed so no filtering was applied to answers. There aremultiple answers per questions and the best one is marked. We considerthe pair Question-Best Answer as an element of the positive training setand Question-Other-Answer as the one of the negative training set. Toderive the negative set, we either randomly select an answer to adifferent but somewhat related question, or formed a query from thequestion and obtained an answer from web search results.

Our second dataset includes the social media. We extractedRequest-Response pairs mainly from postings on Facebook. We also used asmaller portion of LinkedIn.com and vk.com conversations related toemployment. In the social domains the standards of writing are fairlylow. The cohesiveness of text is very limited and the logical structureand relevance frequently absent. The authors formed the training setsfrom their own accounts and also public Facebook accounts available viaAPI over a number of years (at the time of writing Facebook API forgetting messages is unavailable). In addition, we used 860 email threadsfrom Enron dataset. Also, we collected the data of manual responses topostings of an agent which automatically generates posts on behalf ofhuman users-hosts. See Galitsky B., Dmitri Ilvovsky, Nina Lebedeva andDaniel Usikov. Improving Trust in Automation of Social Promotion. AAAISpring Symposium on The Intersection of Robust Intelligence and Trust inAutonomous Systems Stanford Calif. 2014. (“Galitsky 2014”). We formed4000 pairs from the various social network sources.

The third domain is customer complaints. In a typical complaint adissatisfied customer describes his problems with products and serviceas well as the process for how he attempted to communicate theseproblems with the company and how they responded. Complaints arefrequently written in a biased way, exaggerating product faults andpresenting the actions of opponents as unfair and inappropriate. At thesame time, the complainants try to write complaints in a convincing,coherent and logically consistent way (Galitsky 2014); thereforecomplaints serve as a domain with high agreement between requests andresponse. For the purpose of assessing agreement between user complaintand company response (according to how this user describes it) wecollected 670 complaints from planetfeedback.com over 10 years.

The fourth domain is interview by journalist. Usually, the wayinterviews are written by professional journalists is such that thematch between questions and answers is very high. We collected 1200contributions of professional and citizen journalists from such sourcesas datran.com, allvoices.com, huffingtonpost.com and others.

To facilitate data collection, we designed a crawler which searched aspecific set of sites, downloaded web pages, extracted candidate textand verified that it adhered to a question-or-request vs responseformat. Then the respective pair of text is formed. The search isimplemented via Bing Azure Search Engine API in the Web and Newsdomains.

Recognizing valid and invalid answers

Answer classification accuracies are shown in Table 1. Each rowrepresents a particular method; each class of methods in shown in grayedareas.

TABLE 1 Evaluation results Conversation on Customer Interviews bySource/Evaluation Yahoo! Answers Social Networks complaints JournalistsSetting P R F1 P R F1 P R F1 P R F1 Types and counts 55.2 52.9 54.0351.5 52.4 51.95 54.2 53.9 54.05 53 55.5 54.23 for rhetoric reltations ofReq and Resp Entity-based 63.1 57.8 6.33 51.6 58.3 54.7 48.6 57.0 52.4559.2 57.9 53.21 alignment of DT of Req-Resp Maximal common 67.3 64.165.66 70.2 61.2 65.4 54.6 60.0 57.16 80.2 69.8 74.61 sub-DT fo Req andResp Maximal common 68.1 67.2 67.65 68.0 63.8 65.83 58.4 62.8 60.48 77.667.6 72.26 sub-CDT for Req and Resp SVM TK for Parse 66.1 63.8 64.9369.3 64.4 66.8 46.7 61.9 53.27 78.7 66.8 72.24 Trees of individualsentences SVM TK for RST 75.8 74.2 74.99 72.7 77.7 75.11 63.5 74.9 68.7475.7 84.5 79.83 and CA (full parse trees) SVM TK for RR- 76.5 77 76.7574.4 71.8 73.07 64.2 69.4 66.69 82.5 69.4 75.4 DT SVM TK for RR- 80.378.3 79.29 78.6 82.1 80.34 59.5 79.9 68.22 82.7 80.9 81.78 CDT SVM TKfor RR- 78.3 76.9 77.59 67.5 69.3 68.38 55.8 65.9 60.44 76.5 74.0 75.21CDT + sentiment + argumentation features

One can see that the highest accuracy is achieved in journalism andcommunity answers domain and the lowest in customer complaints andsocial networks. We can conclude that the higher is the achievedaccuracy having the method fixed, the higher is the level of agreementbetween Req and Resp and correspondingly the higher the responder'scompetence.

Deterministic family of approaches (middle two rows, local RRsimilarity-based classification) performs about 9% below SVM TK whichindicates that similarity between Req and Resp is substantially lessimportant than certain structures of RR pairs indicative of an RRagreement. It means that agreement between Req and Resp cannot beassessed on the individual basis: if we demand DT(Req) be very similarto DT(Resp) we will get a decent precision but extremely low recall.Proceeding from DT to CDT helps by 1-2% only, since communicativeactions play a major role in neither composing a request nor forming aresponse.

For statistical family of approaches (bottom 5 rows, tree kernels), therichest source of discourse data (SVM TK for RR-DT) gives the highestclassification accuracy, almost the same as the RR similarity-basedclassification. Although SVM TK for RST and CA (full parse trees)included more linguistic data, some part of it (most likely, syntactic)is redundant and gives lower results for the limited training set. Usingadditional features under TK such as sentiment and argumentation doesnot help either: most likely, these features are derived from RR-CDTfeatures and do not contribute to classification accuracy on their own.

Employing TK family of approaches based on CDT gives us the accuracycomparable to the one achieved in classifying DT as correct andincorrect, the rhetoric parsing tasks where the state-of-the-art systemsmeet a strong competition over last few years and derived over 80%accuracy.

Direct analysis approaches in the deterministic family perform ratherweakly, which means that a higher number and a more complicatedstructure of features is required: just counting and taking into accounttypes of rhetoric relations is insufficient to judge on how RR agreewith each other. If two RR pairs have the same types and counts ofrhetoric relations and even communicative actions they can still belongto opposite RR agreement classes in the majority of cases.

Nearest-pair neighbor learning for CDT achieves lower accuracy than SVMTK for CDT, but the former gives interesting examples of sub-trees whichare typical for argumentation, and the ones which are shared among thefactoid data. The number of the former groups of CDT sub-trees isnaturally significantly higher. Unfortunately SVM TK approach does nothelp to explain how exactly the RR agreement problem is solved: it onlygives final scoring and class labels. It is possible but infrequent toexpress a logical argument in a response without communicative actions(this observation is backed up by our data).

Measuring RR Agreement in Evaluation Domains

From the standpoint of evaluation of recognition accuracy, we obtainedthe best method in the previous subsection. Now, having this methodfixed, we will measure RR agreements in our evaluation domains. We willalso show how the general, total agreement delivered by the best methodis correlated with individual agreement criteria such as sentiment,logical argumentation, topics and keyword relevance. Once we use ourbest approach (SVM TK for RR-CDT) for labeling training set, the size ofit can grow dramatically and we can explore interesting properties of RRagreement in various domains. We will discover the contribution of anumber of intuitive features of RR agreement on a larger dataset thanthe previous evaluation.

In this Subsection we intend to demonstrate that the RR pair validityrecognition framework can serve as a measure of agreement between anarbitrary request and response. Also, this recognition framework canassess how strongly various features are correlated with RR pairvalidity.

From the evaluation of recognition accuracy, we obtained the best methodto recognize of the RR pair is valid or not. Now, having thisrecognition method fixed, we will measure RR agreements in ourevaluation domains, and will also estimate how a general, totalagreement delivered by the best method is correlated with individualagreement criteria such as sentiment, logical argumentation, topics andkeyword relevance. Once we use our best approach (SVM TK for RR-CDT) forlabeling training set, the size of it can grow dramatically and we canexplore interesting properties of RR agreement in various domains. Wewill discover on a larger dataset than the previous evaluation, thecontribution of a number of intuitive features of RR agreement. We willmeasure this agreement on a feature-by-feature basis, on a positivetraining dataset of above evaluation only, as a recognition precision(%, Table 2). Notice that recall and the negative dataset is notnecessary for the assessment of agreement.

TABLE 2 Measure of agreement between request and response in fourdomains, % Conversation on Interview Yahoo! Social Customer by AnswersNetworks Complaints Journalists Overall level of 87.2 73.4 67.4 100agreement between requests and response, as determined by SVM TK forRR-CDT Agreement by 61.2 57.3 60.7 70.1 sentiment Agreement by 62.5 60.858.4 66.0 logical argumentation Agreement by topic 67.4 67.9 64.3 82.1as computed by bag-of-words Agreement by topic 80.2 69.4 66.2 87.3 ascomputed by generalization of parse trees Agreement by TK 79.4 70.3 64.791.6 similarity

For example, we estimate as 64.3% the precision of the observation thatthe RR pairs determined by Agreement by topic as computed bybag-of-words approach are valid RR ones in the domain of CustomerComplaints, according to SVM TK for RR-CDT classification.

Agreement by sentiment shows the contribution of proper sentiment matchin RR pair. The sentiment rule includes, in particular, that if thepolarity of RR is the same, response should confirm what request issaying. Conversely, if polarity is opposite, response should attack whatrequest is claiming. Agreement by logical argumentation requires propercommunication discourse where a response disagrees with the claim inrequest.

This data shed a light on the nature of linguistic agreement betweenwhat a proponent is saying and how an opponent is responding. For avalid dialogue discourse, not all agreement features need to be present.However, if most of these features disagree, a given answer should beconsidered invalid, inappropriate and another answer should be selected.Table 2 tells us which features should be used in what degree indialogue support in various domains. The proposed technique cantherefore serve as an automated means of writing quality and customersupport quality assessment.

Chat Bot Applications

A Conversational Agent for Social Promotion (CASP), is an agent that ispresented as a simulated human character which acts on behalf of itshuman host to facilitate and manage her communication for him or her.Galitsky B., Dmitri Ilvovsky, Nina Lebedeva and Daniel Usikov. ImprovingTrust in Automation of Social Promotion. AAAI Spring Symposium on TheIntersection of Robust Intelligence and Trust in Autonomous SystemsStanford Calif. 2014. The CASP relieves its human host from the routine,less important activities on social networks such as sharing news andcommenting on messages, blogs, forums, images and videos of others.Conversational Agent for Social Promotion evolves with possible loss oftrust. The overall performance of CASP with the focus on RR pairagreement, filtering replies mined from the web is evaluated.

On average, people have 200-300 friends or contacts on social networksystems such Facebook and LinkedIn. To maintain active relationshipswith this high number of friends, a few hours per week is required toread what they post and comment on it. In reality, people only maintainrelationship with 10-20 most close friends, family and colleagues, andthe rest of friends are being communicated with very rarely. These notso close friends feel that the social network relationship has beenabandoned. However, maintaining active relationships with all members ofsocial network is beneficial for many aspects of life, from work-relatedto personal. Users of social network are expected to show to theirfriends that they are interested in them, care about them, and thereforereact to events in their lives, responding to messages posted by them.Hence users of social network need to devote a significant amount oftime to maintain relationships on social networks, but frequently do notpossess the time to do it. For close friends and family, users wouldstill socialize manually. For the rest of the network, they would useCASP for social promotion being proposed.

CASP tracks user chats, user postings on blogs and forums, comments onshopping sites, and suggest web documents and their snippets, relevantto a purchase decisions. To do that, it needs to take portions of text,produce a search engine query, run it against a search engine API suchas Bing, and filter out the search results which are determined to beirrelevant to a seed message. The last step is critical for a sensiblefunctionality of CASP, and poor relevance in rhetoric space would leadto lost trust in it. Hence an accurate assessment of RR agreement iscritical to a successful use of CASP.

CASP is presented as a simulated character that acts on behalf of itshuman host to facilitate and manage her communication for her (FIGS.21-22). The agent is designed to relieve its human host from theroutine, less important activities on social networks such as sharingnews and commenting on messages, blogs, forums, images and videos ofothers. Unlike the majority of application domains for simulated humancharacters, its social partners do not necessarily know that theyexchange news, opinions, and updates with an automated agent. Weexperimented with CASP's rhetoric agreement and reasoning about mentalstates of its peers in a number of Facebook accounts. We evaluate itsperformance and accuracy of reasoning about mental states involving thehuman users communicating with it. For a conversational system, usersneed to feel that it properly reacts to their actions, and that what itreplied makes sense. To achieve this in a horizontal domain, one needsto leverage linguistic information to a full degree to be able toexchange messages in a meaningful manner.

CASP inputs a seed (a posting written by a human) and outputs a messageit forms from a content mined on the web and adjusted to be relevant tothe input posting. This relevance is based on the appropriateness interms of content and appropriateness in terms RR agreement, or a mentalstate agreement (for example, it responds by a question to a question,by an answer to a recommendation post seeking more questions, etc.).

FIGS. 21-22 illustrate a chat bot commenting on a posting.

We conduct evaluation of how human users lose trust in CASP and his hostin case of both content and mental state relevance failures. Instead ofevaluating rhetoric relevance, which is an intermediate parameter interms of system usability, we assess how users lose trust in CASP whenthey are annoyed by its rhetorically irrelevant and inappropriatepostings.

TABLE 3 Evaluation results for trust losing scenarios A friend A friendshares with encourages Complexity other friends other friends of theseed A friend A friend that the trist to unfriend a Topic of the andposted complains of unfriends the in CASP is friend with seed messageCASP's host CASP host low CASP Travel and 1 sent 6.2 8.5 9.4 12.8outdoor 2 sent 6.0 8.9 9.9 11.4 3 sent 5.9 7.4 10.0 10.8 4 sent 5.2 6.89.4 10.8 Shopping 1 sent 7.2 8.4 9.9 13.1 2 sent 6.8 8.7 9.4 12.4 3 sent6.0 8.4 10.2 11.6 4 sent 5.5 7.8 9.1 11.9 Events and 1 sent 7.3 9.5 10.313.8 entertainment 2 sent 8.1 10.2 10.0 13.9 3 sent 8.4 9.8 10.8 13.7 4sent 8.7 10.0 11.0 13.8 Job-related 1 sent 3.6 4.2 6.1 6.0 2 sent 3.53.9 5.8 6.2 3 sent 3.7 4.0 6.0 6.4 4 sent 3.2 3.9 5.8 6.2 Personal 1sent 7.1 7.9 8.4 9.0 Life 2 sent 6.9 7.4 9.0 9.5 3 sent 5.3 7.6 9.4 9.34 sent 5.9 6.7 7.5 8.9 Average 6.03 7.5 8.87 10.58

In Table 3 we show the results of tolerance of users to the CASPfailures. After a certain number of failures, friends lose trust andcomplain, unfriend, shares negative information about the loss of trustwith others and even encourage other friends to unfriend a friend who isenabled with CASP. The values in the cell indicate the average number ofpostings with failed rhetoric relevance when the respective event oflost trust occurs. These posting of failed relevance occurred within onemonths of this assessment exercise, and we do not obtain the values forthe relative frequency of occurrences of these postings. On average, 100postings were responded for each user (1-4 per seed posting).

One can see that in various domains the scenarios where users lose trustin CASP are different. For less information-critical domains like traveland shopping, tolerance to failed relevance is relatively high.

Conversely, in the domains taken more seriously, like job related, andwith personal flavor, like personal life, users are more sensitive toCASP failures and the lost of trust in its various forms occur faster.

For all domains, tolerance slowly decreases when the complexity ofposting increases. Users' perception is worse for longer texts,irrelevant in terms of content or their expectations, than for shorter,single sentence or phrase postings by CASP.

A Domain of Natural Language Description of Algorithms

The ability to map natural language to a formal query or commandlanguage is critical to developing more user-friendly interfaces to manycomputing systems such as databases. However, relatively little researchhas addressed the problem of learning such semantic parsers from corporaof sentences paired with their formal-language equivalents. Kate,Rohit., Y. W. Wong, and R. Mooney. Learning to transform natural toformal languages. In AAAI, 2005. Furthermore, to the best of ourknowledge no such research was conducted at discourse level. By learningto transform natural language (NL) to a complete formal language, NLinterfaces to complex computing and AI systems can be more easilydeveloped.

More than 40 years ago, Dijkstra, a Dutch computer scientist whoinvented the concept of “structured programming”, wrote: “I suspect thatmachines to be programmed in our native tongues—be it Dutch, English,American, French, German, or Swahili—are as damned difficult to make asthey would be to use”. The visionary was definitely right—thespecialization and the high accuracy of programming languages are whatmade possible the tremendous progress in the computing and computers aswell. Dijkstra compares the invention of programming languages withinvention of mathematical symbolism. In his words “Instead of regardingthe obligation to use formal symbols as a burden, we should regard theconvenience of using them as a privilege: thanks to them, schoolchildren can learn to do what in earlier days only genius couldachieve”. But four decades years later we keep hitting a wall with theamount of code sitting in a typical industry applications—tens andhundreds of millions lines of code—a nightmare to support and develop.The idiom “The code itself is the best description” became kind of a badjoke.

Natural language descriptions of programs is an area where text rhetoricis peculiar and agreement between statements is essential. We will lookat the common rhetoric representation and also domain-specificrepresentation which maps algorithm description into software code.

FIG. 23 illustrates a discourse tree for algorithm text in accordancewith an aspect. We have the following text and its DT (FIG. 23):

1) Find a random pixel p1.2) Find a convex area a_off this pixel p1 belongs so that all pixels areless than 128.3) Verify that the border of the selected area has all pixels above 128.4) If the above verification succeeds, stop with positive result.Otherwise, add all pixels which are below 128 to the a_off.5) Check that the size of a_off is below the threshold. Then go to 2.Otherwise, stop with negative result.

We now show how to convert a particular sentence into logic form andthen to software code representation. Certain rhetoric relations help tocombine statements obtained as a result of translation of individualsentences.

Verify that the border of the selected area has all pixels above 128.

FIG. 24 illustrates annotated sentences in accordance with an aspect.See FIG. 24 for annotated deconstructions of the pseudocode, 1-1 through1-3.

Converting all constants into variables, we attempt to minimize thenumber of free variables, and not over-constrain the expression at thesame time. Coupled (linked by the edge) arrows show that the sameconstant values (pixel) are mapped into equal variables (Pixel),following the conventions of logic programming. To achieve this, we add(unary) predicates which need to constrain free variables.

1-4) Adding predicates which constrain free variablesepistemic_action(verify) & border(Area) & border(Pixel) & above(Pixel,128) & area(Area)

Now we need to build an explicit expression for quantification all. Inthis particular case it will not be in use, since we use a loopstructure anyway

FIG. 25 illustrates annotated sentences in accordance with an aspect.See FIG. 25 for annotated deconstructions of the pseudocode, 1-5 through2-3.

Finally, we have

2-3) Resultant code fragment

  while (!(Pixel.next( )==null)) { if !(border.belong(Pixel) &&Pixel.above(128)){  bOn=false;  break;  } } Return bOn;

Related Work

Although discourse analysis has a limited number of applications inquestion answering and summarization and generation of text, we have notfound applications of automatically constructed discourse trees. Weenumerate research related to applications of discourse analysis to twoareas: dialogue management and dialogue games. These areas havepotential of being applied to the same problems the current proposal isintended for. Both of these proposals have a series of logic-basedapproaches as well as analytical and machine learning based ones.

Managing Dialogues and Question Answering

If a question and answer are logically connected, their rhetoricstructure agreement becomes less important.

De Boni proposed a method of determining the appropriateness of ananswer to a question through a proof of logical relevance rather than alogical proof of truth. See De Boni, Marco, Using logical relevance forquestion answering, Journal of Applied Logic, Volume 5, Issue 1, March2007, Pages 92-103. We define logical relevance as the idea that answersshould not be considered as absolutely true or false in relation to aquestion, but should be considered true more flexibly in a sliding scaleof aptness. Then it becomes possible to reason rigorously about theappropriateness of an answer even in cases where the sources of answersare incomplete or inconsistent or contain errors. The authors show howlogical relevance can be implemented through the use of measuredsimplification, a form of constraint relaxation, in order to seek alogical proof than an answer is in fact an answer to a particularquestion.

Our model of CDT attempts to combine general rhetoric and speech actinformation in a single structure. While speech acts provide a usefulcharacterization of one kind of pragmatic force, more recent work,especially in building dialogue systems, has significantly expanded thiscore notion, modeling more kinds of conversational functions that anutterance can play. The resulting enriched acts are called dialogueacts. See Jurafsky, Daniel, & Martin, James H. 2000. Speech and LanguageProcessing: An Introduction to Natural Language Processing,Computational Linguistics, and Speech Recognition. Upper Saddle River,N.J.: Prentice Hall. In their multi-level approach to conversation actsTraum and Hinkelman distinguish four levels of dialogue acts necessaryto assure both coherence and content of conversation. See Traum, DavidR. and James F. Allen. 1994. Discourse obligations in dialogueprocessing. In Proceedings of the 32nd annual meeting on Association forComputational Linguistics (ACL '94). Association for ComputationalLinguistics, Stroudsburg, Pa., USA, 1-8. The four levels of conversationacts are: turn-taking acts, grounding acts, core speech acts, andargumentation acts.

Research on the logical and philosophical foundations of Q/A has beenconducted over a few decades, having focused on limited domains andsystems of rather small size and been found to be of limited use inindustrial environments. The ideas of logical proof of “being an answerto” developed in linguistics and mathematical logic have been shown tohave a limited applicability in actual systems. Most current appliedresearch, which aims to produce working general-purpose (“open-domain”)systems, is based on a relatively simple architecture, combiningInformation Extraction and Retrieval, as was demonstrated by the systemspresented at the standard evaluation framework given by the TextRetrieval Conference (TREC) Q/A track.

(Sperber and Wilson 1986) judged answer relevance depending on theamount of effort needed to “prove” that a particular answer is relevantto a question. This rule can be formulated via rhetoric terms asRelevance Measure: the less hypothetical rhetoric relations are requiredto prove an answer matches the question, the more relevant that answeris. The effort required could be measured in terms of amount of priorknowledge needed, inferences from the text or assumptions. In order toprovide a more manageable measure we propose to simplify the problem byfocusing on ways in which constraints, or rhetoric relations, may beremoved from how the question is formulated. In other words, we measurehow the question may be simplified in order to prove an answer.Resultant rule is formulated as follows: The relevance of an answer isdetermined by how many rhetoric constraints must be removed from thequestion for the answer to be proven; the less rhetoric constraints mustbe removed, the more relevant the answer is.

There is a very limited corpus of research on how discovering rhetoricrelations might help in Q/A. Kontos introduced the system which allowedan exploitation of rhetoric relations between a “basic” text thatproposes a model of a biomedical system and parts of the abstracts ofpapers that present experimental findings supporting this model. SeeKontos, John, Ioanna Malagardi, John Peros (2016) Question Answering andRhetoric Analysis of Biomedical Texts in the AROMA System. UnpublishedManuscript.

Adjacency pairs are defined as pairs of utterances that are adjacent,produced by different speakers, ordered as first part and second part,and typed—a particular type of first part requires a particular type ofsecond part. Some of these constraints could be dropped to cover morecases of dependencies between utterances. See Popescu-Belis, Andrei.Dialogue Acts: One or More Dimensions? Tech Report ISSCO Working papern. 62. 2005.

Adjacency pairs are relational by nature, but they could be reduced tolabels (‘first part’, ‘second part’, ‘none’), possibly augmented with apointer towards the other member of the pair. Frequently encounteredobserved kinds of adjacency pairs include the following ones:request/offer/invite→accept/refuse; assess→agree/disagree;blame→denial/admission; question→answer; apology→downplay;thank→welcome; greeting→greeting. See Levinson, Stephen C. 2000.Presumptive Meanings: The Theory of Generalized ConversationalImplicature. Cambridge, Mass.: The MIT Press.

Rhetoric relations, similarly to adjacency pairs, are a relationalconcept, concerning relations between utterances, not utterances inisolation. It is however possible, given that an utterance is asatellite with respect to a nucleus in only one relation, to assign tothe utterance the label of the relation. This poses strong demand for adeep analysis of dialogue structure. The number of rhetoric relations inRST ranges from the ‘dominates’ and ‘satisfaction-precedes’ classes usedby (Grosz and Sidner 1986) to more than a hundred types. Coherencerelations are an alternative way to express rhetoric structure in text.See Scholman, Merel, Jacqueline Evers-Vermeul, Ted Sanders. Categoriesof coherence relations in discourse annotation. Dialogue & Discourse,Vol 7, No 2 (2016)

There are many classes of NLP applications that are expected to leverageinformational structure of text. DT can be very useful is textsummarization. Knowledge of salience of text segments, based onnucleus-satellite relations proposed by Sparck-Jones 1995 and thestructure of relation between segments should be taken into account toform exact and coherent summaries. See Sparck Jones, K. Summarising:analytic framework, key component, experimental method', in SummarisingText for Intelligent Communication, (Ed. B. Endres-Niggemeyer, J. Hobbsand K. Sparck Jones), Dagstuhl Seminar Report 79 (1995). One cangenerate the most informative summary by combining the most importantsegments of elaboration relations starting at the root node. DTs havebeen used for multi-document summaries. See Radev, Dragomir R., HongyanJing, and Malgorzata Budzikowska. 2000. Centroid-based summarization ofmultiple documents: sentence extraction, utility-based evaluation, anduser studies. In Proceedings of the 2000 NAACL-ANLPWorkshop on Automaticsummarization—Volume 4

In the natural language generation problem, whose main difficulty iscoherence, informational structure of text can be relied upon toorganize the extracted fragments of text in a coherent way. A way tomeasure text coherence can be used in automated evaluation of essays.Since a DT can capture text coherence, then yielding discoursestructures of essays can be used to assess the writing style and qualityof essays. Burstein described a semi-automatic way for essay assessmentthat evaluated text coherence. See Burstein, Jill C., LisaBraden-Harder, Martin S. Chodorow, Bruce A. Kaplan, Karen Kukich, ChiLu, Donald A. Rock and Susanne Wolff. (2002).

The neural network language model proposed in (engio 2003 uses theconcatenation of several preceding word vectors to form the input of aneural network, and tries to predict the next word. See Bengio, Yoshua,Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. A neuralprobabilistic language model. J. Mach. Learn. Res. 3 (March 2003),1137-1155. The outcome is that after the model is trained, the wordvectors are mapped into a vector space such that DistributedRepresentations of Sentences and Documents semantically similar wordshave similar vector representations. This kind of model can potentiallyoperate on discourse relations, but it is hard to supply as richlinguistic information as we do for tree kernel learning. There is acorpus of research that extends word2vec models to go beyond word levelto achieve phrase-level or sentence-level representations. For instance,a simple approach is using a weighted average of all the words in thedocument, (weighted averaging of word vectors), losing the word ordersimilar to how bag-of-words approaches do. A more sophisticated approachis combining the word vectors in an order given by a parse tree of asentence, using matrix-vector operations. See R. Socher, C. D. Manning,and A. Y. Ng. 2010. Learning continuous phrase representations andsyntactic parsing with recursive neural networks. In Proceedings of theNIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop.Using a parse tree to combine word vectors, has been shown to work foronly sentences because it relies on parsing.

Many early approaches to policy learning for dialogue systems used smallstate spaces and action sets, and concentrated on only limited policylearning experiments (for example, type of confirmation, or type ofinitiative). The Communicator dataset (Walker et al 2001) is the largestavailable corpus of human-machine dialogues, and has been furtherannotated with dialogue contexts. This corpus has been extensively usedfor training and testing dialogue managers, however it is restricted toinformation requesting dialogues in the air travel domain for a limitednumber of attributes such as destination city. At the same time, in thecurrent work we relied on the extensive corpus of request-response pairsof various natures.

Reichman 1985 gives a formal description and an ATN (AugmentedTransition Network) model of conversational moves, with reference toconventional methods for recognizing the speech act of an utterance. Theauthor uses the analysis of linguistic markers similar to what is nowused for rhetoric parsing such as pre-verbal ‘please’, modalauxiliaries, prosody, reference, clue phrases (such as ‘Yes, but . . . ’(sub-argument concession and counter argument), ‘Yes, and . . . ’(argument agreement and further support), ‘No’ and ‘Yes’(disagreement/agreement), ‘Because . . . ’ (support), etc.) and otherillocutionary indicators. See Reichman, R. 1985. Getting computers totalk like you and me: discourse context, focus and semantics (an ATNmodel). Cambridge, Mass. London: MIT Press.

Given a DT for a text as a candidate answer to a compound query,proposed a rule system for valid and invalid occurrence of the querykeywords in this DT. See Galisky 2015. To be a valid answer to a query,its keywords need to occur in a chain of elementary discourse units ofthis answer so that these units are fully ordered and connected bynucleus—satellite relations. An answer might be invalid if the queries'keywords occur in the answer's satellite discourse units only.

Dialog Games

In an arbitrary conversation, a question is typically followed by ananswer, or some explicit statement of an inability or refusal to answer.There is the following model of the intentional space of a conversation.From the yielding of a question by Agent B, Agent A recognizes Agent B'sgoal to find out the answer, and it adopts a goal to tell B the answerin order to be co-operative. A then plans to achieve the goal, therebygenerating the answer. This provides an elegant account in the simplecase, but requires a strong assumption of co-operativeness. Agent A mustadopt agent B's goals as her own. As a result, it does not explain why Asays anything when she does not know the answer or when she is not readyto accept B's goals.

Litman and Allen introduced an intentional analysis at the discourselevel in addition to the domain level, and assumed a set of conventionalmulti-agent actions at the discourse level. See Litman, D. L. and Allen,J. F. 1987. A plan recognition model for subdialogues in conversation,Cognitive Science, 11: 163-2. Others have tried to account for this kindof behavior using social intentional constructs such as Jointintentions. See Cohen P. R. & Levesque, H. J. 1990. Intention is choicewith commitment, Artificial Intelligence, 42: 213-261. See also Grosz,Barbara J., & Sidner, Candace L. 1986. Attentions, Intentions and theStructure of Discourse. Computational Linguistics, 12(3), 175-204. Whilethese accounts do help explain some discourse phenomena moresatisfactorily, they still require a strong degree of cooperativity toaccount for dialogue coherence, and do not provide easy explanations ofwhy an agent might act in cases that do not support high-level mutualgoals.

Let us imagine a stranger approaching a person and asking, “Do you havespare coins?” It is unlikely that there is a joint intention or sharedplan, as they have never met before. From a purely strategic point ofview, the agent may have no interest in whether the stranger's goals aremet. Yet, typically agents will still respond in such situations. Hencean account of Q/A must go beyond recognition of speaker intentions.Questions do more than just provide evidence of a speaker's goals, andsomething more than adoption of the goals of an interlocutor is involvedin formulating a response to a question.

Mann proposed a library of discourse level actions, sometimes calleddialogue games, which encode common communicative interactions. SeeMann, William and Sandra Thompson. 1988. Rhetorical structure theory:Towards a functional theory of text organization. Text-InterdisciplinaryJournal for the Study of Discourse, 8(3):243-281. To be co-operative, anagent must always be participating in one of these games. So if aquestion is asked, only a fixed number of activities, namely thoseintroduced by a question, are co-operative responses. Games provide abetter explanation of coherence, but still require the agents torecognize each other's intentions to perform the dialogue game. As aresult, this work can be viewed as a special case of the intentionalview. Because of this separation, they do not have to assumeco-operation on the tasks each agent is performing, but still requirerecognition of intention and co-operation at the conversational level.It is left unexplained what goals motivate conversational co-operation.

Coulthard and Brazil suggested that responses can play a dual role ofboth response and new initiation: Initiation{circumflex over( )}(Re-Initiation){circumflex over ( )}Response{circumflex over( )}(Follow-up). See Coulthard, R. M. and Brazil D. 1979. Exchangestructure: Discourse analysis monographs no. 5. Birmingham: TheUniversity of Birmingham, English Language Research. Exchanges canconsist of two to four utterances. Also, follow-up itself could befollowed up. Opening moves indicate the start of the exchange sometimes,which do not restrict the type of the next move. Finally, closing movessometimes occur which are not necessarily a follow-up. When theseobservations are added to their formula one ends up with:

(Open){circumflex over ( )}Initiation{circumflex over( )}(Re-Initiation){circumflex over ( )}Response{circumflex over( )}(Feedback){circumflex over ( )}(Follow-up){circumflex over( )}(Close)

This now can deal with anything from two to seven more exchanges.

FIG. 26 illustrates discourse acts of a dialogue in accordance with anaspect. Tsui (1994) characterizes the discourse acts according to athree-part transaction. Her systems of choice for Initiating, Respondingand Follow-up are shown in FIG. 26 on the top, middle and bottomcorrespondingly.

FIG. 27 illustrates discourse acts of a dialogue in accordance with anaspect.

The classification problem of valid vs invalid RR pairs is alsoapplicable to the task of complete dialogue generation beyond questionanswering and automated dialogue support. Popescu presented alogic-based rhetorical structuring component of a natural languagegenerator for human-computer dialogue. The pragmatic and contextualaspects are taken into account communicating with a task controllerproviding domain and application-dependent information, structured infully formalized task ontology. In order to achieve the goal ofcomputational feasibility and generality, discourse ontology has beenbuilt and a number of axioms introducing constraints for rhetoricrelations have been proposed.

For example, the axiom specifying the semantics of topic(α) is givenbelow:

topic(α)::=ExhaustiveDecomposition(i, j; vi, wj) & memberOf(vi, K (α)) &memberOf(ωj,Ω)(∃k: equals(vk,ωj) & memberOf(vk,K(α))).where K(α) the clause logically expressing the semantics of theutterance a.

The notion of topic of an utterance is defined here in terms of sets ofobjects in the domain ontology, referred to in a determined manner inthe utterance. Hence, the topic relations between utterances arecomputed using the task/domain ontology, handled by the task controller.

As an instance of such rule one can consider

topic(β)::=ExhaustiveDecomposition(book, read, good time(‘14 h’), goodtime(‘monday’), t+);−good time(θ)::=∃γ,π: ¬Disjoint(topic(γ), topic(π)) &smaller(tα,tπ) &((SubclassOf(θ, Δtα) v equals(θ,Δtα)) & π: equals(Δtπ,θ);where t+ is “future and ‘new’”.

Rhetoric Relations and Argumentation

Frequently, the main means of linking questions and answers is logicalargumentation. There is an obvious connection between RST andargumentation relations which tried to learn in this study. There arefour types of relations: the directed relations support, attack, detail,and the undirected sequence relation. The support and attack relationsare argumentative relations, which are known from related work. SeePeldszus, A. and Stede, M. 2013. From Argument Diagrams to ArgumentationMining in Texts: A Survey. Int. J of Cognitive Informatics and NaturalIntelligence 7(1), 1-31). The latter two correspond to discourserelations used in RST. The argumentation sequence relation correspondsto “Sequence” in RST, the argumentation detail relation roughlycorresponds to “Background” and “Elaboration”.

Argumentation detail relation is important because many cases inscientific publications, where some background information (for examplethe definition of a term) is important for understanding the overallargumentation. A support relation between an argument component Resp andanother argument component Req indicates that Resp supports (reasons,proves) Req. Similarly, an attack relation between Resp and Req isannotated if Resp attacks (restricts, contradicts) Req. The detailrelation is used, if Resp is a detail of Req and gives more informationor defines something stated in Req without argumentative reasoning.Finally, we link two argument components (within Req or Resp) with thesequence relation, if the components belong together and only make sensein combination, i.e., they form a multi-sentence argument component.

We observed that using SVM TK one can differentiate between a broadrange of text styles (Galitsky 2015), including ones withoutargumentation and ones with various forms of argumentation. Each textstyle and genre has its inherent rhetoric structure which is leveragedand automatically learned. Since the correlation between text style andtext vocabulary is rather low, traditional classification approacheswhich only take into account keyword statistics information could lackthe accuracy in the complex cases. We also performed text classificationinto rather abstract classes such as the belonging to language-objectand metalanguage in literature domain and style-based documentclassification into proprietary design documents. See Galitsky, B,Ilvovsky, D. and Kuznetsov S O. Rhetoric Map of an Answer to CompoundQueries Knowledge Trail Inc. ACL 2015, 681-686 Evaluation of textintegrity in the domain of valid vs invalid customer complains (thosewith argumentation flow, non-cohesive, indicating a bad mood of acomplainant) shows the stronger contribution of rhetoric structureinformation in comparison with the sentiment profile information.Discourse structures obtained by RST parser are sufficient to conductthe text integrity assessment, whereas sentiment profile-based approachshows much weaker results and also does not complement strongly therhetoric structure ones.

An extensive corpus of studies has been devoted to RST parsers, but theresearch on how to leverage RST parsing results for practical NLPproblems is limited to content generation, summarization and search(Jansen et al 2014). DTs obtained by these parsers cannot be useddirectly in a rule-based manner to filter or construct texts. Therefore,learning is required to leverage implicit properties of DTs. This studyis a pioneering one, to the best of our knowledge, that employsdiscourse trees and their extensions for general and open-domainquestion answering, chatbots, dialogue management and text construction.

Dialogue chatbot systems need to be capable of understanding andmatching user communicative intentions, reason with these intentions,build their own respective communication intentions and populate theseintentions with actual language to be communicated to the user.Discourse trees on their own do not provide representation for thesecommunicative intents. In this study we introduced the communicativediscourse trees, built upon the traditional discourse trees, which canbe massively produced nowadays on one hand and constitute a descriptiveutterance-level model of a dialogue on the other hand. Handlingdialogues via machine learning of communicative discourse trees allowedus to model a wide array of dialogue types of collaboration modes andinteraction types (planning, execution, and interleaved planning andexecution).

Statistical computational learning approaches offer several keypotential advantages over the manual rule-based hand-coding approach todialogue systems development:

-   -   data-driven development cycle;    -   provably optimal action policies;    -   a more accurate model for the selection of responses;    -   possibilities for generalization to unseen states;    -   reduced development and deployment costs for industry.

Comparing inductive learning results with the kernel-based statisticallearning, relying on the same information allowed us to perform moreconcise feature engineering than either approach would do.

An extensive corpus of literature on RST parsers does not address theissue of how the resultant DT will be employed in practical NLP systems.RST parsers are mostly evaluated with respect to agreement with the testset annotated by humans rather than its expressiveness of the featuresof interest. In this work we focus on interpretation of DT and exploredways to represent them in a form indicative of an agreement ordisagreement rather than neutral enumeration of facts.

To provide a measure of agreement for how a given message in a dialogueis followed by a next message, we used CDTs, which now include labelsfor communicative actions in the form of substituted VerbNet frames. Weinvestigated the discourse features that are indicative of correct vsincorrect request-response and question-answer pairs. We used twolearning frameworks to recognize correct pairs: deterministic,nearest-neighbor learning of CDTs as graphs, and a tree kernel learningof CDTs, where a feature space of all CDT sub-trees is subject to SVMlearning.

The positive training set was constructed from the correct pairsobtained from Yahoo Answers, social network, corporate conversationsincluding Enron emails, customer complaints and interviews byjournalists. The corresponding negative training set was created byattaching responses for different, random requests and questions thatincluded relevant keywords so that relevance similarity between requestsand responses are high. The evaluation showed that it is possible torecognize valid pairs in 68-79% of cases in the domains of weakrequest-response agreement and 80-82% of cases in the domains of strongagreement. These accuracies are essential to support automatedconversations. These accuracies are comparable with the benchmark taskof classification of discourse trees themselves as valid or invalid, andalso with factoid question-answering systems.

We believe this study is the first one that leverages automaticallybuilt discourse trees for question answering support. Previous studiesused specific, customer discourse models and features which are hard tosystematically collect, learn with explainability, reverse engineer andcompare with each other. We conclude that learning rhetoric structuresin the form of CDTs are key source of data to support answering complexquestions, chatbots and dialogue management.

Argumentation Detection using Communicative Discourse Trees

Aspects described herein use communicative discourse trees to determinewhether a text contains argumentation. Such an approach can be useful,for example, for chatbots to be able to determine whether a user isarguing or not. When a user attempts to provide an argument forsomething, a number of argumentation patterns can be employed. Anargument can be a key point of any communication, persuasive essay, orspeech.

A communicative discourse tree for a given text reflects theargumentation present in the text. For example, the basic points ofargumentation are reflected in the rhetoric structure of text where anargument is presented. A text without argument has different rhetoricstructures. See Moens, Marie-Francine, Erik Boiy, Raquel Mochales Palau,and Chris Reed. 2007. Automatic detection of arguments in legal texts.In Proceedings of the 11th International Conference on ArtificialIntelligence and Law, ICAIL '07, pages 225-230, Stanford, Calif., USA.)Additionally, argumentation can differ between domains. For example, forproduct recommendation, texts with positive sentiments are used toencourage a potential buyer to make a purchase. In the political domain,the logical structure of sentiment versus argument versus agency is muchmore complex.

Machine learning can be used in conjunction with communicative discoursetrees to determine argumentation. Determining argumentation can betackled as a binary classification task in which a communicativediscourse tree that represents a particular block of text is provided toa classification model. The classification model returns a prediction ofwhether the communicative discourse tree is in a positive class or anegative class. The positive class corresponds to texts with argumentsand the negative class corresponds to texts without arguments. Aspectsdescribed herein can perform classification based on different syntacticand discourse features associated with logical argumentation. In anexample, for a text to be classified as one containing an argument, thetext is similar to the elements of the first class to be assigned tothis class. To evaluate the contribution of our sources, two types oflearning can be used: nearest neighbor and statistical learningapproaches.

Nearest Neighbor (kNN) learning uses explicit engineering of graphdescriptions. The similarity measured is the overlap between the graphof a given text and that of a given element of training set. Instatistical learning, aspects learn structures with implicit features.

Generally, the machine learning approaches estimate the contribution ofeach feature type and the above learning methods to the problem ofargument identification including the presence of opposing arguments(Stab and Gurevych, 2016). More specifically, aspects use the rhetoricrelations and how the discourse and semantic relations work together inan argumentation detection task.

Whereas sentiment analysis is necessary for a broad range of industrialapplications, its accuracy remains fairly low. Recognition of a presenceof an argument, if done reliably, can potentially substitute someopinion mining tasks when one intends to differentiate a strongopinionated content from the neutral one. Argument recognition resultcan then serve as a feature of sentiment analysis classifier,differentiating cases with high sentiment polarity from the neutralones, ones with low polarity.

Example of Using Communicative Discourse Trees to Analyse Argumentation

The following examples are introduced to illustrate the value of usingcommunicative discourse trees to determine the presence of argumentationin text. The first example discusses Theranos, a healthcare company thathoped to make a revolution in blood tests. Some sources, including theWall Street Journal, claimed that the company's conduct was fraudulent.The claims were made based on the whistleblowing of employees who leftTheranos. At some point FDA got involved. In 2016, some of the publicbelieved Theranos' position, that the case was initiated by Theranoscompetitors who felt jealous about the efficiency of blood testtechnique promised by Theranos. However, using argumentation analysis,aspects described herein illustrate that the Theranos argumentationpatterns mined at their website were faulty. In fact, a fraud case waspushed forward, which led to the massive fraud verdict. According to theSecurities and Exchange Commission, Theranos CEO Elizabeth Holmes raisedmore than $700 million from investors “through an elaborate, years-longfraud” in which she exaggerated or made false statements about thecompany's technology and finances.

Considering the content about Theranos, if a user leans towards Theranosand not its opponents, then an argumentation detection system attemptsto provide answers favoring Theranos position. Good arguments of itsproponents, or bad arguments of its opponents would also be useful inthis case. Table 4 shows the flags for various combinations of agency,sentiments and argumentation for tailoring search results for a givenuser with certain preferences of entity A vs entity B. The right grayedside of column has opposite flags for the second and third row. For thefourth row, only the cases with generally accepted opinion sharingmerits are flagged for showing.

A chatbot can use the information in Table 4 to personalize responses ortailor search results or opinionated data to user expectations. Forexample, a chatbot can consider political viewpoint when providing newsto a user. Additionally, personalizing responses is useful for productrecommendations. For example, a particular user might prefer skis oversnowboards as evidenced by a user's sharing of stories of people who donot like snowboarders. In this manner, the aspects described hereinenable a chatbot can behave like a companion, by showing empathy andensuring that the user does not feel irritated by the lack of commonground with the chatbot.

TABLE 4 Request from user Proper Improper argumentation ImproperPositive Negative Proper argumentation by a argumentation Answersentiment sentiment argumentation that A is proponent of by a opponenttype for A for B that A is right wrong A of A Favoring + + + + + + − Arather than B Favoring + B rather than A Equal + + + treatment of A andB

Continuing the Theranos example, a RST representation of the argumentsis constructed and aspects can observe if a discourse tree is capable ofindicating whether a paragraph communicates both a claim and anargumentation that backs it up. Additional information is added to adiscourse tree such that it is possible to judge if it expresses anargumentation pattern or not. According to the Wall Street Journal, thisis what happened: “Since October [2015], the Wall Street Journal haspublished a series of anonymously sourced accusations that inaccuratelyportray Theranos. Now, in its latest story (“U.S. Probes TheranosComplaints,” December 20), the Journal once again is relying onanonymous sources, this time reporting two undisclosed and unconfirmedcomplaints that allegedly were filed with the Centers for Medicare andMedicaid Services (CMS) and U.S. Food and Drug Administration (FDA).”(Carreyrou, 2016)

FIG. 28 depicts an exemplary communicative discourse tree in accordancewith an aspect. FIG. 28 depicts discourse tree 2800, communicativeaction 2801 and communicative action 2802. More specifically, discoursetree 2800 represents the following paragraph: “But Theranos hasstruggled behind the scenes to turn the excitement over its technologyinto reality. At the end of 2014, the lab instrument developed as thelinchpin of its strategy handled just a small fraction of the tests thensold to consumers, according to four former employees.” As can be seen,when arbitrary communicative actions are attached to the discourse tree2800 as labels of terminal arcs, it becomes clear that the author istrying to bring her point across and not merely sharing a fact. Asshown, communicative action 2801 is a “struggle” and communicativeaction 2802 is “develop.”

FIG. 29 depicts an exemplary communicative discourse tree in accordancewith an aspect. FIG. 29 depicts discourse tree 2900, which representsthe following text: “Theranos remains actively engaged with itsregulators, including CMS and the FDA, and no one, including the WallStreet Journal, has provided Theranos a copy of the alleged complaintsto those agencies. Because Theranos has not seen these allegedcomplaints, it has no basis on which to evaluate the purportedcomplaints.” But as can be seen, from only the discourse tree andmultiple rhetoric relations of elaboration and a single instance ofbackground, it is unclear whether an author argues with his opponents orenumerates some observations. Relying on communicative actions such as“engaged” or “not see”, CDT can express the fact that the author isactually arguing with his opponents

FIG. 30 depicts an exemplary communicative discourse tree in accordancewith an aspect. FIG. 30 depicts discourse tree 3000, which representsthe following text, in which Theranos is attempting to get itself offthe hook: “It is not unusual for disgruntled and terminated employees inthe heavily regulated health care industry to file complaints in aneffort to retaliate against employers for termination of employment.Regulatory agencies have a process for evaluating complaints, many ofwhich are not substantiated. Theranos trusts its regulators to properlyinvestigate any complaints.”

As can be seen, to show the structure of arguments, discourse relationsare necessary but insufficient, and speech acts (communicative actions)are necessary but insufficient as well. For the paragraph associatedwith FIG. 30, it is necessary to know the discourse structure ofinteractions between agents, and what kind of interactions they are.More specifically, differentiation is needed between a neutralelaboration (which does not include a communicative action) and anelaboration relation which includes a communicative action with asentiment such as “not provide” which is correlated with an argument.Note that the domain of interaction (e.g., healthcare) is not necessary,nor are the subjects of these interactions (the company, the journal,the agencies), or what the entities are. However, mental,domain-independent relations between these entities are useful.

FIG. 31 depicts an exemplary communicative discourse tree in accordancewith an aspect. FIG. 31 depicts discourse tree 3100, which representsthe following text for Theranos' argument that the opponent's argumentsare faulty: “By continually relying on mostly anonymous sources, whiledismissing concrete facts, documents, and expert scientists andengineers in the field provided by Theranos, the Journal denies itsreaders the ability to scrutinize and weigh the sources' identities,motives, and the veracity of their statements.”

From the commonsense reasoning standpoint, Theranos, the company, hastwo choices to confirm the argument that its tests are valid: (1)conduct independent investigation, comparing their results with thepeers, opening the data to the public, confirming that their analysisresults are correct; and (2) defeat the argument by its opponent thattheir testing results are invalid, and providing support for the claimthat their opponent is wrong. Obviously, the former argument is muchstronger and usually the latter argument is chosen when the agentbelieves that the former argument is too hard to implement. On one hand,the reader might agree with Theranos that WSJ should have provided moreevidence for its accusations against the company. On the other hand, thereader perhaps disliked the fact that Theranos selects the latterargument type (2) above, and therefore the company's position is fairlyweak. One reason that that Theranos' argument is weak is because thecompany tries to refute the opponent's allegation concerning thecomplaints about Theranos's services from clients. Theranos' demand forevidence by inviting WSJ to disclose the sources and the nature of thecomplaints is weak. A claim is that a third-party (independentinvestigative agent) would be more reasonable and conclusive. However,some readers might believe that the company's argument (burden of proofevasion) is logical and valid. Note that an argumentation assessorcannot identify the rhetorical relations in a text by relying on textonly. Rather, the context of the situation is helpful in order to graspthe arguer's intention.

In a second example, an objective of the author is to attack a claimthat the Syrian government used chemical weapon in the spring of 2018.FIG. 32 depicts an example communicative discourse tree in accordancewith an aspect. FIG. 32 depicts communicative discourse tree 3200 forthis second example.

Considering the example, an acceptable proof would be to share a certainobservation, associated from the standpoint of peers, with the absenceof a chemical attack. For example, if it is possible to demonstrate thatthe time of the alleged chemical attack coincided with the time of avery strong rain, that would be a convincing way to attack this claim.However, since no such observation was identified, the source, RussiaToday, resorted to plotting a complex mental states concerning how theclaim was communicated, where it is hard to verify most statements aboutthe mental states of involved parties. The following shows theelementary discourse units split by the discourse parser: [Whatever theDouma residents,][who had first-hand experience of the shooting of thewater][dousing after chemical attack video,][have to say,][their wordssimply do not fit into the narrative][allowed in the West,][analyststold RT.] [Footage of screaming bewildered civilians and children][beingdoused with water,][presumably to decontaminate them,][was a key part inconvincing Western audiences][that a chemical attack happened in Douma.][Russia brought the people][seen in the video][to Brussels,][where theytold anyone][interested in listening][that the scene was staged.] [Theirtestimonies, however, were swiftly branded as bizarre and underwhelmingand even an obscene masquerade][staged by Russians.] [They refuse to seethis as evidence,][obviously pending][what the OPCW team is going tocome up with in Douma], [Middle East expert Ammar Waqqaf said in aninterview with RT.] [The alleged chemical incident,][without anyinvestigation, has already become a solid fact in the West,][which theUS, Britain and France based their retaliatory strike on.]

Note that the text above does not find counter-evidence for the claim ofthe chemical attack it attempts to defeat. Instead, the text states thatthe opponents are not interested in observing this counter-evidence. Themain statement of this article is that a certain agent “disallows” aparticular kind of evidence attacking the main claim, rather thanproviding and backing up this evidence. Instead of defeating a chemicalattack claim, the article builds a complex mental states conflictbetween the residents, Russian agents taking them to Brussels, the Westand a Middle East expert.

FIG. 33 depicts an example communicative discourse tree in accordancewith an aspect. FIG. 33 depicts communicative discourse tree 3300 foranother controversial story, a Trump-Russia link acquisition (BBC 2018).For a long time, the BBC was unable to confirm the claim, so the storyis repeated and over and over again to maintain a reader expectationthat it would be instantiated one day. There is neither confirmation norrejection that the dossier exists, and the goal of the author is to makethe audience believe that such dossier exists without misrepresentingevents. To achieve this goal, the author can attach a number ofhypothetical statements about the existing dossier to a variety ofmental states to impress the reader in the authenticity and validity ofthe topic.

As depicted in FIGS. 32 and 33, many rhetorical relations are associatedwith mental states. Mental states are sufficiently complex that it ishard for a human to verify a correctness of the main claim. Thecommunicative discourse tree shows that an author is attempting tosubstitute a logical chain which would back up a claim with complexmental states. By simply looking at the CDTs depicted in FIGS. 32 and 33without reading the associated text sufficient to see that the line ofargument is faulty.

Handling Heated Arguments

FIG. 34 depicts an example communicative discourse tree in accordancewith an aspect. FIG. 34 depicts communicative discourse tree 3400 for anexample of a heated argumentation. Specifically, the following text,represented by communicative discourse tree 3400 illustrates an exampleof a CDT for a heated argumentation of a customer treated badly by acredit card company American Express (Amex) in 2007. The communicativediscourse tree 3400 shows a sentiment profile. A sentiment profile is asentiment value attached to an indication of a proponent (in this case,“me”) and an opponent (in this case, “Amex”). As can be seen, theproponent is almost always positive and the opponent is negativeconfirms the argumentation flow of this complaint. Oscillating sentimentvalues would indicate that there is an issue with how an author providesargumentation.

The text is split into logical chunks is as follows: [I'm another one ofthe many][that has been carelessly mistreated by American Express.] [Ihave had my card since 2004 and never late.] [In 2008][they reduced mycredit limit from $16,600 to $6,000][citing several false excuses.][Only one of their excuses was true—other credit card balances.] [Theyalso increased my interest rate by 3%][at the same time.] [I have neverbeen so insulted by a credit card company.] [I used to have a creditscore of 830, not anymore, thanks to their unfair credit practices.][They screwed my credit score.] [In these bad economic times you'dthink][they would appreciate consistent paying customers like us][but Iguess][they are just so full of themselves.] [I just read today][thattheir CEO stated][that they will be hurt less than theircompetitors][because 80 percent of their revenues][are generated fromfees. That][explains their callous, arrogant, unacceptable creditpractices.] [It seems][they have to screw every cardholder][they canbefore the new law becomes effective.] [Well America, let's learn fromour appalling experience][and stop using our American Express creditcard][so we can pay it off !].

FIG. 35 depicts an example communicative discourse tree in accordancewith an aspect. FIG. 35 depicts communicative discourse tree 3500 thatrepresents a text advising on how to behave communicating an argument:“When a person is in the middle of an argument, it can be easy to getcaught up in the heat of the moment and say something that makes thesituation even worse. Nothing can make someone more frenzied andhysterical than telling them to calm down. It causes the other person tofeel as if one is putting the blame for the elevation of the situationon them. Rather than actually helping them calm down, it comes off aspatronizing and will most likely make them even angrier.” FIG. 35 is anexample of meta-argumentation. A meta-argumentation is an argumentationon how to conduct heated argumentation, which can be expressed by thesame rhetorical relations.

Using a Machine Learning Model to Determine Argumentation

As discussed, classification application 102 can detect argumentation intext. FIG. 36 depicts an exemplary process for using machine learning todetermine argumentation in accordance with an aspect.

At block 3601, process 3600 involves accessing text comprisingfragments. Classification application 102 can text from differentsources such input text 130, or Internet-based sources such as chat,Twitter, etc. Text can consist of fragments, sentences, paragraphs, orlonger amounts.

At block 3602, process 3600 involves creating a discourse tree from thetext, the discourse tree including nodes and each nonterminal noderepresenting a rhetorical relationship between two of the fragments andeach terminal node of the nodes of the discourse tree is associated withone of the fragments. Classification application 102 creates discoursein a substantially similar manner as described in block 1502 in process1500.

At block 3603, process 3600 involves matching each fragment that has averb to a verb signature, thereby creating a communicative discoursetree. Classification application 102 creates discourse in asubstantially similar manner as described in blocks 1503-1505 in process1500.

At block 3604, process 3600 involves determining whether thecommunicative discourse tree includes argumentation by applying aclassification model trained to detect argumentation to thecommunicative discourse tree. The classification model can use differentlearning approaches. For example, the classification model can use asupport vector machine with tree kernel learning. Additionally, theclassification model can use nearest neighbor learning of maximal commonsub-trees.

As an example, classification application 102 can use machine learningto determine similarities between the communicative discourse treeidentified at block 3603 and one or more communicative discourse treesfrom a training set of communicative discourse trees. Classificationapplication 102 can select an additional communicative discourse treefrom a training set that includes multiple communicative discoursetrees. Training can be based on the communicative discourse tree havinga highest number of similarities with the additional communicativediscourse tree. Classification application 102 identifies whether theadditional communicative discourse tree is from a positive set or anegative set. The positive set is associated with text containingargumentation and the negative set is associated with text containing noargumentation. Classification application 102 determines based on thisidentification whether the text contains an argumentation or noargumentation.

Evaluation of Logical Argument Detection

To evaluate argumentation detection, a positive dataset is created froma few sources to make it non-uniform and pick together different styles,genres and argumentation types. First we used a portion of data whereargumentation is frequent, e.g. opinionated data from newspapers such asThe New York Times (1400 articles), The Boston Globe (1150 articles),Los Angeles Times (2140) and others (1200). Textual customer complaintsare also used. Additionally, the text style & genre recognition datasetis used (Lee, 2001). This dataset has a specific dimension associatedwith argumentation (the section [ted] “Emotional speech on a politicaltopic with an attempt to sound convincing”). And we finally add sometexts from standard argument mining datasets where presence of argumentsis established by annotators: “Fact and Feeling” dataset (Oraby et al.,2015), 680 articles and dataset “Argument annotated essays v.2” (Staband Gurevych, 2016), 430 articles.

For the negative dataset, Wikipedia (3500 articles), factual newssources (Reuters feed with 3400 articles, and also (Lee, 2001) datasetincluding such sections of the corpus as [tells] (450 articles),“Instructions for how to use software” (320 articles); [tele],“Instructions for how to use hardware” (175 articles); [news], “Apresentation of a news article in an objective, independent manner” (220articles), and other mixed datasets without argumentation (735 articles)can be used.

Both positive and negative datasets include 8800 texts. An average textsize was 400 words (always above 200 and below 1000 words). We usedAmazon Mechanical Turk to confirm that the positive dataset includesargumentation in a commonsense view, according to the employed workers.Twelve workers who had the previous acceptance score of above 85% wereassigned the task to label. For manual confirmation of the presence andabsence of arguments, we randomly selected representative from each set(about 10%) and made sure they properly belong to a class with above 95%confidence. We avoided sources where such confidence was below 95%. Forfirst portion of texts which were subject to manual labeling weconducted an assessment of inter-annotator agreement and observed thatit exceeded 90%. Therefore for the rest of annotations we relied on asingle worker per text. For the evaluation we split out dataset into thetraining and test part in proportion of 4:1.

Specific Argumentation Pattern Dataset

The purpose of this argumentation dataset is to collect textualcomplaints where the authors use a variety of argumentation means toprove that they are victims of businesses. Customer complainants areemotionally charged texts which include descriptions of problems theyexperienced with certain businesses. Raw complaints are collected fromPlanetFeedback.com for a number of banks submitted in years 2006-2010.Four hundred complaints are manually tagged with respect to thefollowing parameters related to argumentation:

-   -   perceived complaint validity,    -   argumentation validity    -   presence of specific argumentation patter    -   and detectable misrepresentation.

Judging by complaints, most complainants are in genuine distress due toa strong deviation between what they expected from a service, what theyreceived and how it was communicated. Most complaint authors reportincompetence, flawed policies, ignorance, indifference to customer needsand misrepresentation from the customer service personnel.

The authors are frequently exhausted communicative means available tothem, confused, seeking recommendation from other users and adviseothers on avoiding particular financial service. The focus of acomplaint is a proof that the proponent is right and her opponent iswrong, resolution proposal and a desired outcome.

Multiple argumentation patterns are used in complaints:

-   -   The most frequent is a deviation from what has happened from        what was expected, according to common sense. This pattern        covers both valid and invalid argumentation (a valid pattern).    -   The second in popularity argumentation patterns cites the        difference between what has been promised (advertised,        communicated) and what has been received or actually occurred.        This pattern also mentions that the opponent does not play by        the rules (valid).    -   A high number of complaints are explicitly saying that bank        representatives are lying. Lying includes inconsistencies        between the information provided by different bank agents,        factual misrepresentation and careless promises (valid).    -   Another reason complaints arise is due to rudeness of bank        agents and customer service personnel. Customers cite rudeness        in both cases, when the opponent point is valid or not (and        complaint and argumentation validity is tagged accordingly).        Even if there is neither financial loss nor inconvenience the        complainants disagree with everything a given bank does, if they        been served rudely (invalid pattern).    -   Complainants cite their needs as reasons bank should behave in        certain ways. A popular argument is that since the government        via taxpayers bailed out the banks, they should now favor the        customers (invalid).

This dataset includes more emotionally-heated complaints in comparisonwith other argument mining datasets. For a given topic such asinsufficient funds fee, this dataset provides many distinct ways ofargumentation that this fee is unfair. Therefore, our dataset allows forsystematic exploration of the topic-independent clusters ofargumentation patterns and observe a link between argumentation type andoverall complaint validity. Other argumentation datasets including legalarguments, student essays (Stab and Gurevych 2017), internet argumentcorpus (Abbot et al., 2016), fact-feeling dataset (Oraby et al., 2016)and political debates have a strong variation of topics so that it isharder to track a spectrum of possible argumentation patterns per topic.Unlike professional writing in legal and political domains, authenticwriting of complaining users have a simple motivational structure, atransparency of their purpose and occurs in a fixed domain and context.In the dataset used in this study, the arguments play a critical rulefor the well-being of the authors, subject to an unfair charge of alarge amount of money or eviction from home. Therefore, the authorsattempt to provide as strong argumentation as possible to back up theirclaims and strengthen their case.

If a complaint is not truthful it is usually invalid: either a customercomplains out of a bad mood or she wants to get a compensation. However,if the complaint is truthful it can easily be invalid, especially whenarguments are flawed. When an untruthful complaint has validargumentation patterns, it is hard for an annotator to properly assignit as valid or invalid. Three annotators worked with this dataset, andinter-annotator agreement exceeds 80%.

Evaluation Setup and Results

For the Nearest Neighbor classification, we used Maximal commonsub-graph for DT approach as well as Maximal common sub-graph for CAapproach based on scenario graphs built on CAs extracted from text(Table 5). For SVM TK classification, we employed the tree kernellearning of parse thickets approach, where each paragraph is representedby a parse thicket that includes exhaustive syntactic and discourseinformation. We also used SVM TK for DT, where CA information is nottaken into account.

Our family of pre-baseline approaches are based on keywords and keywordsstatistics. For Naïve Bayes approach, we relied on WEKA framework (Hallet al., 2009). Since mostly lexical and length-based features arereliable for finding poorly-supported arguments (Stab and Gurevych2017), we used non-NERs as features together with the number of tokensin the phrase which potentially expresses argumentation. Also, NERcounts was used as it is assumed to be correlated with the strength ofan argument. Even if these features are strongly correlated witharguments, they do not help to understand the nature of howargumentation is structure and communicated in language, as expressed byCDTs.

TABLE 5 Evaluation results. Nearest Neighbor-based detection Method &Improvement over the Source Precision Recall F1 baseline Keywords 57.253.1 55.07 0.87 Naïve Bayes 59.4 55.0 57.12 0.91 DT 65.6 60.4 62.89 1.00CA 62.3 59.5 60.87 0.97 CDT (DT + CA) 83.1 75.8 79.28 1.26

TABLE 6 Evaluation results. SVM TK-based detection Improvement overMethod & Source Precision Recall F1 the baseline RST and CA 77.2 74.475.77 1.00 (full parse trees) DT 63.6 62.8 63.20 0.83 CDT 82.4 77.079.61 1.05

A naïve approach is just relying on keywords to figure out a presence ofargumentation. Usually, a couple of communicative actions so that atleast one has a negative sentiment polarity (related to an opponent) aresufficient to deduce that logical argumentation is present. This naïveapproach is outperformed by the top performing CDT approach by 29%. ANaïve Bayes classifier delivers just 2% improvement.

One can observe that for nearest neighbor learning DT and CA indeedcomplement each other, delivering accuracy of the CDT 26% above theformer and 30% above the latter. Just CA delivered worse results thanthe standalone DT (Table 6). As can be seen, SVM TK of CDT outperformsSVM TK for RST+CA and full syntactic features (the SVM TK baseline) by5%. This is due to feature engineering and relying on less data but morerelevant one that the baseline.

TABLE 7 Evaluation results for each positive dataset versus combinednegative dataset (SVM TK) Text style & Newspaper Textual genre Method &opinionated Complaints, recognition Fact and Source data, F1 F1 dataset,F1 Feeling Keywords 52.3 55.2 53.7 54.8 Naïve Bayes 57.1 58.3 57.2 59.4DT 66.0 63.6 67.9 66.3 CA 64.5 60.3 62.5 60.9 CDT (DT + CA) 77.1 78.880.3 79.2

Nearest neighbor learning for CDT achieves slightly lower accuracy thanSVM TK for CDT, but the former gives interesting examples of sub-treeswhich are typical for argumentation, and the ones which are shared amongthe factual data. The number of the former groups of CDT sub-trees isnaturally significantly higher. Unfortunately SVM TK approach does nothelp to explain how exactly the argument identification problem issolved. It only gives final scoring and class labels. It is possible,but infrequent to express a logical argument without CAs. Thisobservation is backed up by our data.

It is worth mentioning that our evaluation settings are close toSVM-based ranking of RST parses. This problem is formulated asclassification of DTs into the set of correct trees, close to manuallyannotated trees, and incorrect ones. Our settings are a bit differentbecause they are better adjusted to smaller datasets. Notice thatargument detection improvement proceeding from DT to CDT demonstratesthe adequateness of our extension of RST by speech act—relatedinformation.

Table 7 shows the SVM TK argument detection results per source. As apositive set, we now take individual source only. The negative set isformed from the same sources but reduced in size to match the size of asmaller positive set. The cross-validation settings are analogous to ourassessment of the whole positive set.

We did not find correlation between the peculiarities of a particulardomain and contribution of discourse-level information to argumentdetection accuracy. At the same time, all these four domains showmonotonic improvement when we proceed from Keywords and Naïve Bayes toSVM TK. Since all four sources demonstrate the improvement of argumentdetection rate due to CDT, we conclude that the same is likely for othersource of argumentation-related information.

TABLE 8 Evaluation results for each positive dataset versus combinednegative dataset (SVM TK) The difference between what has been Deviationpromised from what (advertised, Rudeness has communicated) Saying ofbank happened and what has that agents and from been received bankcustomer Method & what was or actually representatives service Sourceexpected occurred are lying personnel Keywords 51.7 53.7 58.5 59.0 NaïveBayes 53.4 55.9 61.3 65.8 DT 61.9 58.5 68.5 68.6 CA 58.8 59.4 63.4 61.6CDT 70.3 68.4 84.7 83.0 (DT + CA)

Pattern—specific argumentation detection results are shown in Table 8.We compute the accuracy of classification as a specific pattern vs otherpatterns and a lack of argumentation. The first and second type ofargument is harder to recognize (by 7-10% below the general argument)and the third and fourth type is easier to detect (exceeds the generalargument accuracy by 3%).

These argument recognition accuracies are comparable withstate-of-the-art of argumentation mining techniques. One study conductedan analysis of texts containing 128 premise conclusion pairs andobtained 63-67% F-measure, determining the directionality of inferentialconnections in argumentation. See Lawrence, John and Chris Reed. MiningArgumentative Structure from Natural Language text using AutomaticallyGenerated Premise-Conclusion Topic Models. Proceedings of the 4thWorkshop on Argument Mining, pages 39-48. 2017. Bar-Haim et al. showthat both accuracy and coverage of argument stance recognition (what issupporting and what is defeating a claim) can be significantly improvedto 69% F-measure through automatic expansion of the initial lexicon. SeeBar-Haim, Roy Lilach Edelstein, Charles Jochim and Noam Slonim.Improving Claim Stance Classification with Lexical Knowledge Expansionand Context Utilization. Proceedings of the 4th Workshop on ArgumentMining, pages 32-38. 2017. Aker et al. offer a comparative analysis ofthe performance of different supervised machine learning methods andfeature sets on argument mining tasks, achieving 81% F-measure fordetecting argumentative sentences and 59% for argument structureprediction task. See Aker, Ahmet, Alfred Sliwa, Yuan Ma, Ruishen LiuNiravkumar Borad, Seyedeh Fatemeh Ziyaei, Mina Ghbadi What works andwhat does not: Classifier and feature analysis for argument mining.Proceedings of the 4th Workshop on Argument Mining, pages 91-96. 2017.As to the argumentation segmentation of an argument text into argumentunits and their non-argumentative counterparts, Ajjour et alachievee 88%using Bi-LSTM for essays and 84% for editorials. See Ajjour, Yamen,Wei-Fan Chen, Johannes Kiesel, Henning Wachsmuth and Benno Stein. UnitSegmentation of Argumentative Texts. Proceedings of the 4th Workshop onArgument Mining, pages 118-128, 2017. Taking into account complexitiesof argument mining tasks, these classification accuracies are comparablewith the current study but lack an exploration of causation ofargumentation via discourse-level analysis. Hence this study proposesmuch more straight-forward feature engineering of general argumentationand its specific patterns.

CDT Construction

Although splitting into EDUs works reasonably well, assignment of RSTrelation is noisy and in some domain its accuracy can be as low as 50%.However, when the RST relation label is random, it does notsignificantly drop the performance of our argumentation detection systemsince a random discourse tree will be less similar to elements ofpositive or negative training set, and most likely will not participatein positive or negative decision. To overcome the noisy input problem,more extensive training datasets are required so that the number ofreliable, plausible discourse tree is high enough to cover cases to beclassified. As long as this number is high enough, a contribution ofnoisy, improperly built discourse trees is low.

There is a certain systematic deviation from correct, intuitivediscourse trees obtained by discourse parsers. In this section we aregoing to evaluate if there is a correlation between the deviation inCDTs and our training sets. We allow for a possibility that CDTsdeviation for texts with argumentation is stronger than the one for thetexts without argumentation.

For each source, we calculated the number of significantly deviatedCDTs. For the purpose of this assessment we considered a CDT to bedeviated if more than 20% of rhetoric relations is determinedimproperly. We do not differentiate between the specific RST relationsassociated with argumentation such as attribution and contrast. Thedistortion evaluation dataset is significantly smaller than thedetection dataset since substantial manual efforts is required and thetask cannot be submitted to Amazon Mechanical Turk workers.

TABLE 9 Investigation if deviation in CDT construction is dependent onthe class being separated Positive Negative Significantly Significantlytraining training deviating DTs deviating DTs set set for Positive forNegative Source size size training set, % training set, % Newspapers 3030 15.4 ± 4.60 21.3 ± 3.85 Text style 40 40 18.2 ± 5.21 20.7 ± 4.84 &genre recognition dataset Fact and 25 25 22.3 ± 4.92 16.9 ± 5.40 FeelingArgument 30 30 19.6 ± 3.43 17.5 ± 4.27 annotated essays

One can observe that there is no obvious correlation between therecognition classes and the rate of CDT distortion (Table 9). Hence weconclude that the training set of noisy CDTs can be adequately evaluatedwith respect to argumentation detection. As can be seen, there is astrong correlation between these noisy CDTs and a presence of a logicalargument.

Sentiment

Because reliable sentiment detection in an arbitrary domain ischallenging, we focus on a particular sentiment-related feature such aslogical argumentation with a certain polarity. Detection of logicalargumentation can help improve the performance for detection ofsentiment detection. We formulate sentiment detection problem at thelevel of paragraphs. We only detect sentiment polarity.

Classifying sentiment on the basis of individual words can be misleadingbecause atomic sentiment carriers can be modified (weakened,strengthened, or reversed) based on lexical, discourse, or contextualfactors. Words interact with each other to yield an expression-levelpolarity. For example, the meaning of a compound expression is afunction of the meaning of its parts and of the syntactic rules by whichthey are combined. Hence, taking account of more linguistic structurethan required by RST is what motivates our combination of these insightsfrom various discourse analysis models. Our hypothesis is that it ispossible to calculate the polarity values of larger syntactic elementsof a text in a very accurate way as a function of the polarities oftheir sub-constituents, in a way similar to the ‘principle ofcompositionality’ in formal semantics. In other words, if the meaning ofa sentence is a function of the meanings of its parts then the globalpolarity of a sentence is a function of the polarities of its parts. Forexample, we can attribute a negative trait to the verb “reduce”, but apositive polarity in “reduce the risk” even though “risk” is negative initself (cf. the negative polarity in “reduce productivity”). Thispolarity reversal is only captured once we extend the analysis beyondthe sentence level to calculate the global polarity of text as a whole.Hence any polarity conflict is resolved as a function of the globalmeaning of text, based on textual and contextual factors. The polarityweights are not properties of individual elements of text, but thefunction of properties operating at the level of cohesion and coherencerelations latent in the syntactic, discourse and pragmatic levels ofdiscourse analysis.

A number of studies has showed that discourse-related information cansuccessfully improve the performance of sentiment analysis, Forinstance, one can reweigh the importance of EDUs based on their relationtype or depth (Hogenboom et al, 2015a) in the DT. Some methods prune thediscourse trees at certain thresholds to yield a tree of fixed depthbetween two and four levels. Other approaches train machine learningclassifiers based on the relation types as input features (Hogenboom etal, 2015b). Most research in RDST for sentiments try to map the DTstructure onto mathematically simpler representations, since it isvirtually impossible to encode unstructured data of arbitrary complexityin a fixed-length vector (Markle-HuB et al 2017).

FIG. 37 is a fragment of a discourse tree in accordance with an aspect.FIG. 37 depicts discourse tree 3700, which represents the followingtext. We use the following two sentences to show that thenucleus—satellite relation does matter to determine a sentiment for anentity: [Although the camera worked well,][I could not use it because ofthe viewfinder], which represents a negative sentiment about the camera;and [The camera worked well], [although the viewfinder wasinconvenient], which represents a positive sentiment about the camera.

For evaluation of sentiment detection, we used a dataset of positive andnegative, genuine and fake travelers' review of Chicago area hotels. SeeM. Ott, C. Cardie, and J. T. Hancock. 2013. Negative Deceptive OpinionSpam. In Proceedings of the 2013 Conference of the North AmericanChapter of the Association for Computational Linguistics: Human LanguageTechnologies. The authors compile the dataset for the purpose ofdifferentiating between genuine and fake reviews. It turns out thatfakeness of a review is not strongly correlated with a presence of alogical argument. Fake reviews, created by Mechanical Turn workers, backup opinions of the authors in the same way real travelers do. The testcorpus contains four groups 400 reviews of 1-3 paragraphs each. 1) 400truthful positive reviews from TripAdvisor; 2) 400 deceptive positivereviews from Mechanical Turk; 3) 400 truthful negative reviews fromExpedia, Hotels.com, Orbitz, Priceline, TripAdvisor and 4) 400 deceptivenegative reviews from Mechanical Turk.

As a baseline approach we use Stanford NLP Sentiment. We obtain thesentence-level polarity and aggregate it to the paragraphs level.Usually if an opinion is positive, the author just enumerates what shelikes. However, if an opinion is negative, in many cases the authorwould try to back it up, perform a comparison, explanation, argumentsfor why he is right and his assessment is adequate.

Hence the rule for integration of a default and argumentation-basedsentiment detectors are as follows (Table 10). This rule is orientedtowards consumer review data and would need modifications to bettertreat other text genre.

TABLE 10 Integration rule Decision Decision of a logical argumentdetector of a default 1 (possibly sentiment 0 (no some 2 (strongdetector argument) argument) argument) −1 0 −1 −1 0 0 0 −1 +1 +1 +1 −1

The case below is a borderline positive review, and it can easily beflipped to become negative: “Like all hotels in Chicago, this hotelcaters to wealthy and/or business clients with very high parking price.However, if you are aware of that prior to arrival, it's not a big deal.It makes sense to find a different place to park the car and bring yourown snacks for the room. It would be nice though if hotels such as theSwissotel had a fridge in the room for guest use. Staff was veryhelpful. Overall, if I can get a good rate again, I'll stay at theSwissotel the next time I am in Chicago.” This text looks overall like anegative review from the DT standpoint. Most reviews with similar DTsare negative.

FIG. 38 depicts a discourse tree for a borderline review in accordancewith an aspect. FIG. 38 depicts discourse tree 3800 for a borderlinereview. A borderline review is negative from the discourse point of viewand neutral from the reader's standpoint.

Extending Compositionality Semantics Towards Discourse

Let us look how the sentiment in first sentence is assessed by SemanticCompositionality model. See R. Socher, A. Perelygin, J. Wu, J. Chuang,C. Manning, A. Ng and C. Potts. Recursive Deep Models for SemanticCompositionality Over a Sentiment Treebank. Conference on EmpiricalMethods in Natural Language Processing (EMNLP 2013). Judging byindividual words and their composition, it is hard to understand that‘high price’ have a negative sentiment value here. In the movie databasefor training, ‘high’ is assigned the positive sentiment, and most likely‘high price’ is not tagged as negative. Even if ‘high price’ isrecognized as negative, it would be hard to determine how the rest ofthe tree would affect it, such as the phrase ‘wealthy and/or businessclients’. Notice that in the movie domain the words of this phrase arenot assigned adequate sentiments either.

It is rather hard to determine the sentiment polarity of this sentencealone, given its words and phrasing. Instead, taking into account thediscourse of the consecutive sentences, the overall paragraph sentimentand the one of the given sentence can be determined with a higheraccuracy.

FIG. 39 depicts a discourse tree for a sentence showing compositionalsemantic approach to sentiment analysis in accordance with an aspect.FIG. 39 depicts discourse tree 3900.

We state that sentiment analysis benefiting from the ‘compositionalsemantics’ insights would accurately assign polarity sentiment in theexample above if the analysis captures not only word ‘high’ (assignednegative sentiment polarity), phrase ‘high price’ (with negativesentiment polarity) or sentence level structure ‘Like all . . . price’(where sentiment polarity is difficult to determine because we need toread the whole text for a global sentiment polarity attribution).Sentiment analysis is calculated based on global polarity, not dependenton individual elements of the sentence, but more interestingly, on thediscourse level structure (macro-structure). For example, “highreliability” is neutral in “I want a car with high reliability” becausethough it is a positive property, it does not refer to any specific car.

Results

The baseline system (Socher et al., 2013) is trained on a differentdomain than the test domain since our evaluation of sentiment detectionis domain-independent.

The results of sentiment analysis achieved by the hybrid compositionalsemantics and discourse analysis are shown in Table 11. In the first rowwe show the accuracy of the baseline system on our data. In the secondgrayed row we show the improvement by means of the hybrid system. Thisimprovement is achieved by discovering overall negative sentiment at theparagraph level in case of recognized presence of argumentation. In someof these cases the negative sentiment is implicit and can only bedetected indirectly from the discourse structure, where individual wordsdo not indicate negative sentiments.

TABLE 11 Evaluation of sentiment analysis Data source and methodPrecision Recall F Baseline (Standord NLP) 62.7 68.3 65.38 Hybridsentiment detector (Stanford 79.3 81.0 80.14 NLP + SVM TK for CDT)Sentiment detector via SVM TK for DT 67.5 69.4 68.44 Sentiment detectorvia SVM TK for CDT 69.8 68.3 69.04 Untruthful opinion data detector,positive 81.2 74.9 77.92 reviews (SVM TK for parse thicket) Untruthfulopinion data detector, negative 78.3 74.7 76.46 reviews (for parsethicket)

We investigate a stand-alone SVM TK sentiment recognition system withvarious representations (rows three to five). CDT representationoutperforms parse thickets and DT ones. With simpler representationwhich does not take into account discourse-level information at all,sentiment recognition accuracy is fairly low (not shown).

We also explored whether fake opinionated text have different rhetoricstructure to genuine one. See Jindal and Liu, Opinion Spam and Analysis,Department of Computer Science, University of Illinois at Chicago, 2008.Jindal and Liu addressed the problem of detection of disruptive opinionspam: obvious instances that are easily identified by a human reader,e.g., advertisements, questions, and other irrelevant or non-opiniontexts. (Ott et al. investigated potentially more insidious type ofopinion spam such as deceptive opinion spam, ones that have beendeliberately written to sound authentic, in order to deceive the reader.See M. Ott, Y. Choi, C. Cardie, and J. T. Hancock. 2011. FindingDeceptive Opinion Spam by Any Stretch of the Imagination. In Proceedingsof the 49th Annual Meeting of the Association for ComputationalLinguistics: Human Language Technologies. Fake reviews were written byAmazon Mechanical Turk workers. The instructions asked the workers toassume that they are employed by a hotel's marketing department, and topretend that they are asked to write a fake review (as if they were acustomer) to be posted on a travel review website; additionally, thereview needs to sound realistic and portray the hotel in a positivelight. A request for negative reviews is done analogously.

Although our SVM TK system did not achieve performance of 90%, the taskof detection of fake review texts was performed (at 76-77% accuracy, twobottom greyed rows) by the universal text classification system, thesame which extracts arguments and assesses sentiments polarity.

Validation of Arguments

Aspects of the present disclosure validate argumentation. To beconvincing, a text or an utterance includes a valid argument.Classification application 102 extracts an argumentation structure froma body of text and represents the argumentation via a communicativediscourse tree (CDT). Subsequently, classification application 102 canverify that the claim, or target claim, in the text is valid, i.e., isnot logically attacked by other claims, and is consistent with externaltruths, i.e., rules. With domain knowledge, the validity of a claim canbe validated. However, in some cases, domain knowledge may beunavailable and other domain-independent information, such as writingstyle and writing logic, are used.

Certain aspects enable applications such as Customer RelationshipManagement (CRM). CRM addresses handling customer complaints (Galitskyand de la Rosa 2011). In customer complaints, authors are upset withproducts or services they received, as well as how an issue wascommunicated by customer support. Complainants frequently writecomplaints in a very strong, emotional language, which may distort thelogic of argumentation and therefore make a judgment on complaintvalidity difficult. Both affective and logical argumentation is heavilyused.

To facilitate improved autonomous agents, certain aspects useargument-mining, which is a linguistic-based, and logical validation ofan argument, which is logic based. The concept of automaticallyidentifying argumentation schemes was first discussed in (Walton et al.,2008). Ghosh et al. (2014) investigates argumentation discoursestructure of a specific type of communication—online interactionthreads. Identifying argumentation in text is connected to the problemof identifying truth, misinformation and disinformation on the web(Pendyala and Figueira, 2015, Galitsky 2015, Pisarevskaya et al 2015).In (Lawrence and Reed, 2015) three types of argument structureidentification are combined: linguistic features, topic changes andmachine learning. As explained further herein, some aspects employDefeasible Logic Programming (DeLP) (Garcia and Simari, 2004; Alsinet etal., 2008) in conjunction with communicative discourse trees.

FIG. 40 depicts an exemplary process 4000 for validating arguments inaccordance with an aspect. Classification application 102 can performprocess 4000.

At block 4001, process 4000 involves accessing text that includesfragments. At block 4001, process 4000 performs substantially similarsteps as described in block 3601 of process 3600. Examples of textinclude a paragraph, sentence, and an utterance.

At block 4002, process 4000 involves identifying a presence ofargumentation in a subset of the text by creating a communicativediscourse tree from the text and applying a classification model trainedto detect argumentation to the communicative discourse tree. At block4002, process 4000 performs substantially similar steps as described inblocks 3602-3604 of process 3600. Other methods of argumentationdetection can be used.

A block 4003, process 4000 involves evaluating the argumentation byusing a logic system. Classification application 102 can use differenttypes of logic systems to evaluate the argumentation. For example,Defeasible Logic Programming (DeLP) can be used. FIG. 42 depictsexemplary operations that can implement block 4003. For illustrativepurposes, process 4000 is discussed with respect to FIG. 41.

FIG. 41 depicts an exemplary communicative discourse tree for anargument in accordance with an aspect. FIG. 41 includes communicativediscourse tree 4101. Communicative discourse tree 4101 includes node4120 and other nodes, some of which are labeled with communicativeactions 4110-4117.

In an example, a judge hears an eviction case and wants to make ajudgment on whether rent was provably paid (deposited) or not (denotedas rent receipt). An input is a text where a defendant is expressing hispoint. Communicative discourse tree 4101 represents the following text:“The landlord contacted me, the tenant, and the rent was requested.However, I refused the rent since I demanded repair to be done. Ireminded the landlord about necessary repairs, but the landlord issuedthe three-day notice confirming that the rent was overdue. Regretfully,the property still stayed unrepaired.”

FIG. 42 depicts an exemplary method for validating arguments usingdefeasible logic programming in accordance with an aspect. Defeasiblelogic programming (DeLP) is a set of facts, strict rules Π of the form(A:−B), and a set of defeasible rules 4 of the form A-<B, whose intendedmeaning is “if B is the case, then usually A is also the case.” LetP=(Π, Δ) be a DeLP program and L a ground literal. Strict rules cannotbe changed, even based on opinion. In contrast, a defeasible rule can befalse in some cases.

In the above example, underlined words form the clause in DeLP, and theother expressions can form the facts. An example of a fact is “rentrefused,” i.e. that a landlord refused rent. An example of a strict ruleis “the earth is flat.” An example of a defeasible rule is“rent_receipt-<rent_deposit_transaction,” which means that, usually if“rent_deposit_transaction” then “rent_receipt” (rent is received). But adefeasible rule may not always be true, for example, if the rent isdeposited in the wrong account or there is an error at the bank.

Classification application 102 can use results from the communicativediscourse tree developed at block 4002 as inputs for the DeLP. Thecommunicative discourse tree indicates valuable information, such as howthe facts are inter-connected by defeasible rules. Elementary discourseunits of the CDT that are of rhetorical relation type “contrast” andcommunicative actions that are of type “disagree” indicate defeasiblerules.

At block 4201, method 4200 involves creating a fixed part of a logicsystem. The fixed part of the logic system includes one or more claimterms and one or more domain definition clauses. Domain definitionclauses are associated with a domain of the text and can include legal,scientific terms, and commonsense knowledge in a particular domain. Ascientific example is “if a physical body is moving with acceleration,it is subject to a physical force.” In the area of landlord-tenant law,an example of a standard definition is: “if repair is done->home ishabitable and appliances are working.”

Continuing the above example, the text contains a target claim to beevaluated “rent_receipt,” i.e. “was the rent received?” Classificationapplication 102 also extracts the following clause“repair_is_done-<rent_refused” from the text “refused the rent since Idemanded repair to be done.”

At block 4202, method 4200 involves creating a variable part of thelogic system by determining a set of defeasible rules and a set offacts. Classification application 102 determines, from the communicativediscourse tree, a set of defeasible rules by extracting, from thecommunicative discourse tree, one or more of (i) an elementary discourseunit that is a rhetorical relation type contrast and (ii) acommunicative action that is of a class type disagree. The classdisagree includes actions such as “deny,” “have different opinion, “notbelieve,” “refuse to believe,” “contradict,” “diverge,” “deviate,”“counter,” “differ,” “dissent,” “be dissimilar.” Other examples arepossible.

Classification application 102 determines the following defeasiblerules: rent_receipt-<rent_deposit_transaction,

rent_deposit_transaction-<contact_tenant.┐ rent_deposit_transaction-<contact_tenant, three_days_notice_is_issued.┐ rent_deposit_transaction-<rent_is_overdue.┐ repair_is_done-<rent_refused, repair_is_done.repair_is_done-<rent_is_requested.┐ rent_deposit_transaction-<tenant_short_on_money, repair_is_done.┐ repair_is_done-<repair_is_requested.┐ repair_is_done-<rent_is_requested.┐ repair_is_requested-<stay_unrepaired. ┐repair_is_done-<stay_unrepaired.

Additionally, classification application 102 determines additional factsfrom communicative actions that are of type “disagree.” Continuing theexample, and referring back to FIG. 41, classification application 102determines the following facts from the subjects of the communicativeactions of the CDT: contact_tenant (communicative action 4111),rent_is_requested (communicative action 4112), rent_refused(communicative action 4113), stay_unrepaired (communicative action4114), remind_about_repair (communicative action 4115),three_days_notice_is_issued (communicative action 4116), andrent_is_overdue (communicative action 4117).

At block 4203, method 4200 involves determining a defeasible derivationcomprising a set of non-contradictory defeasible rules from thedefeasible set of rules. A defeasible derivation of L from P consists ofa finite sequence L₁, L₂, . . . , L_(n)=L of ground literals, such thateach literal L_(i) is in the sequence because: (a) L_(i) is a fact in Π,or (b) there exists a rule R^(i) in P (strict or defeasible) with headL_(i) and body B₁, B₂, . . . , B_(k) and every literal of the body is anelement L_(j) of the sequence appearing before L_(j) (j<i). Let h be aliteral, and P=(Π, Δ) a DeLP program. We say that <A, h> is an argumentfor h, if A is a set of defeasible rules of Δ, such that:

1. there exists a defeasible derivation for h from =(Π∪A);

2. the set (Π∪A) is non-contradictory; and

3. A is minimal: there is no proper subset A₀ of A such that A₀satisfies conditions (1) and (2).

Hence an argument <A, h> is a minimal non-contradictory set ofdefeasible rules, obtained from a defeasible derivation for a givenliteral h associated with a program P. As discussed above, a minimalsubset means that no subset exists that satisfies conditions 1 and 2.

At block 4204, method 4200 involves creating one or more defeaterarguments from the set of facts. Defeaters are arguments which can be intheir turn attacked by other arguments, as is the case in a humandialogue. An argumentation line is a sequence of arguments where eachelement in a sequence defeats its predecessor. In the case of DeLP,there are a number of acceptability requirements for argumentation linesin order to avoid fallacies (such as circular reasoning by repeating thesame argument twice).

Defeater arguments can be formed in the following manner. For example,argument <A₁, h₁> attacks <A₂, h₂> iff (if and only if) there exists asub-argument <A, h> of <A₂, h₂> (A ⊆A₁) such that h and h₁ areinconsistent (i.e. Π∪{h, h₁} derives complementary literals). We willsay that <A₁, h₁> defeats <A₂, h₂> if <A₁, h₁> attacks <A₂, h₂> at asub-argument <A, h> and <A₁, h₁> is strictly preferred (or notcomparable to)<A, h>. In the first case we will refer to <A₁, h₁> as aproper defeater, whereas in the second case it will be a blockingdefeater.

At block 4205, method 4200 involves constructing, from the defeasiblederivation, a dialectic tree including a root node representing theargument and leaf nodes that represent the defeater arguments. Targetclaims can be considered DeLP queries which are solved in terms ofdialectical trees, which subsumes all possible argumentation lines for agiven query. The definition of dialectical tree provides us with analgorithmic view for discovering implicit self-attack relations inusers' claims. Let <A₀, h₀> be an argument (target claim) from a programP. For discussion purposes, block 4205 is discussed with respect to FIG.43.

FIG. 43 depicts an exemplary dialectic tree in accordance with anaspect. FIG. 43 depicts the dialectical tree for the text developedabove. FIG. 43 includes dialectical tree 4300, which includes root node4301 and nodes 4302-4307. Dialectical tree 4300 is based on <A₀, h₀>,which is defined as follows:

-   -   1. The root of the tree (root node 4301) is labeled with <A₀,        h₀>    -   2. Let N be a non-root vertex of the tree labeled <A_(n), h_(n)>        and A=[<A₀, h₀>, <A₁, h₁>, . . . , <A_(n), h_(n)>] (the sequence        of labels of the path from the root to N). Let [<B₀, q₀>, <B₁,        q₁>, . . . <B_(k), q_(k)>] all attack <A_(n), h_(n)>. For each        attacker <B_(i), q_(i)> with acceptable argumentation line        [Λ,<B_(i), q_(i)>], we have an arc between N and its child        N_(i).

A labeling on the dialectical tree can be then performed as follows:

-   -   1. All leaves (nodes 4302-4307) are to be labeled as U-nodes        (undefeated nodes).    -   2. Any inner node is to be labeled as a U-node whenever all of        its associated children nodes are labeled as D-nodes.    -   3. Any inner node is to be labeled as a D-node whenever at least        one of its associated children nodes is labeled as U-node.

At block 4206, method 4200 involves evaluating the dialectic tree byrecursively evaluating the defeater arguments.

In the DeLP example, the literal rent_receipt is supported by <A,rent_receipt>=<{(rent_receipt-<rent_deposit_transaction),(rent_deposit_transaction-<tenant_short_on_money)}, rent_receipt> andthere exist three defeaters for it with three respective argumentationlines:

-   (1) <B₁, ┐ rent_deposit_transaction=<{(┐    rent_deposit_transaction-<tenant_short_on_money,    three_days_notice_is_issued)}, rent_deposit_transaction>.-   (2) >B₂, ┐ rent_deposit_transaction>=<{(┐    rent_deposit_transaction-<tenant_short_on_money, repair_is_done),    (repair_is_done-<rent_refused)}, rent_deposit_transaction>.-   (3) <B₃, ┐ rent_deposit_transaction=<{(┐    rent_deposit_transaction-<rent_is_overdue)},    rent_deposit_transaction>.

(1) and (2) are proper defeaters and the last one is a blockingdefeater. Observe that the first argument structure has thecounter-argument, <{rent_deposit_transaction-<tenant_short_on_money},

-   -   rent_deposit_transaction),        but it is not a defeater because the former is more specific.        Thus, no defeaters exist and the argumentation line ends there.

B3 above has a blocking defeater<{(rent_deposit_transaction-<tenant_short_on_money)},rent_deposit_transaction>,

which is a disagreement sub-argument of <A, rent_receipt> and it cannotbe introduced since it gives rise to an unacceptable argumentation line.B₂ has two defeaters which can be introduced: <C₁, ┐ repair_is_done >,where C₁={(┐ repair_is_done-<rent refused, repair_is_done),(repair_is_done-<rent_is_requsted)}, a proper defeater, and <C₂, ┐repair_is_done >, where C₂={(┐ repair_is_done-<repair_is_requested)} isa blocking defeater. Hence one of these lines is further split into two;C₁ has a blocking defeater that can be introduced in the line <D₁, ┐repair_is_done >, where D₁=<{(┐ repair_is_done-<stay_unrepaired)}. D₁and C₂ have a blocking defeater, but they cannot be introduced becausethey make the argumentation line inacceptable. Hence the staterent_receipt cannot be reached, as the argument supporting the literalrent_receipt, is not warranted.

At block 4207, method 4200 involves responsive to determining that noneof the defeater arguments are contradictory with the defeasiblederivation, identifying the claim supported by the argument as valid. Adetermination that no contradictory arguments exits indicates that theclaim is valid, whereas a determination that contradictory argumentsexists indicates that the claim is invalid. Classification 102 can thenperform an action based on the validation, such as providing differentanswers to a user device based on the validity of the claim.

Argument Validation Results

Argument validation is evaluated based on argument detection (bylinguistic means) and then validation (logical means). A dataset of 623legal cases scraped from Landlord vs Tenant (2018) is formed. Each yearthis website provides more than 700 summaries of recent landlord-tenantcourt cases and agency decisions. Landlord v. Tenant covers more than adozen courts and agencies, including the NYC Civil Court, NYS Divisionof Housing and Community Renewal (DHCR), NYC Environmental ControlBoard, and many more. The website allows users to get access to theirdynamic database of cases that go back to 1993 and the New York Landlordv. Tenant newsletter archives, as well as to run searches for designatedcase summaries. Full-text case decisions and opinion letters are alsoavailable from this source.

A typical case abstract is like the following: “Tenants complained of areduction in building-wide services. They said that the building superdidn't make needed repairs as requested and that landlord had refused toperform repairs in their apartment. They also complained about buildingaccessibility issues. Among other things, the building side door walkwaywas reconstructed and made narrower. This made it hard to navigate awheelchair through that doorway. The DRA ruled against tenants, whoappealed and lost.”

Firstly, we extract sentences containing argumentation and then attemptto find a claim being communicated, from out DeLP ontology. The claim tobe validated in the above example is repair_is_done. We then subjectthis claim to validation. We obtain the claim validity value from thetags on the web page assigned by the judge who heard the case, such asrent_reduction_denied. Table 12, below shows evaluation results beingcommunicated with argumentation in landlord versus tenant case texts.

TABLE 12 Method/sources P R F1 Bag-of-words 53.1 56.8 54.89 WEKA-NaïveBayes 60.1 58.8 59.44 SVM TK for RST and 75.7 75.5 75.60 CA (full parsetrees) SVM TK for DT 61.2 63.8 62.47 SVM TK for CDT 81.9 77.5 79.64

For the argument detection task, we use this landlord vs tenant as apositive training set. As a negative dataset, we use various textsources which should contain neither argumentation nor opinionated data.We used Wikipedia, factual news sources, and also the component of (Lee,2001) dataset that includes such sections of the corpus as: [′tells],instructions for how to use software; [′tele], instructions for how touse hardware, and [news], a presentation of a news article in anobjective, independent manner, and others. Further details on thenegative, argumentation-free data sets are available in (Galitsky et al2018 and Chapter 10).

A baseline argument detection approach relies on keywords and syntacticfeatures to detect argumentation (Table 13.8). Frequently, a coordinatedpair of communicative actions (so that at least one has a negativesentiment polarity related to an opponent) is a hint that logicalargumentation is present. This naïve approach is outperformed by the topperforming TK learning CDT approach by 29%. SVM TK of CDT outperformsSVM TK for RST+CA and RST+full parse trees (Galitsky, 2017) by about 5%due to noisy syntactic data which is frequently redundant forargumentation detection.

SVM TK approach provides acceptable F-measure but does not help toexplain how exactly the affective argument identification problem issolved, providing only final scoring and class labels. Nearest neighbormaximal common sub-graph algorithm is much more fruitful in this respect(Galitsky et al., 2015). Comparing the bottom two rows, we observe thatit is possible, but infrequent to express an affective argument withoutCAs.

Assessing logical arguments extracted from text, we were interested incases where an author provides invalid, inconsistent, self-contradictingcases. That is important for chatbot as a front end of a CRM systemsfocused on customer retention and facilitating communication with acustomer (Galitsky et al., 2009). The domain of residential real estatecomplaints was selected and a DeLP thesaurus was built for this domain.Automated complaint processing system can be essential, for example, forproperty management companies in their decision support procedures(Constantinos et al., 2003).

TABLE 13 Evaluation results for the whole argument validation pipelineF1 of F1 of Types of complaints P R validation total Single rhetoricrelation of type contrast 87.3 15.6 26.5 18.7 Single communicativeaction of type 85.2 18.4 30.3 24.8 disagree Couple of rhetoric relationincluding 86.2 22.4 35.6 23.9 contrast, cause, temporal, attributionCouple of rhetoric relation above 82.4 20.7 33.1 25.1 plus couple ofcommunication actions disagree, deny responsibility, argue Two or threespecific relations or 80.2 20.6 32.8 25.4 communicative actions Four andabove specific relations or 86.3 16.5 27.7 21.7 communicative actions

In our validity assessment we focus on target features related to how agiven complaint needs to be handled, such as compensation_required,proceed_with_eviction, rent_receipt and others.

In the first and second rows, we show the results of the simplestcomplaint with a single rhetoric relation such as contrast and a singleCA indicating an extracted argumentation attack relation respectively.In the third and fourth rows we show the validation results for legalcases with two non-default rhetorical relations and two CAs of thedisagreement type, correspondingly. In the fifth row we assesscomplaints of average complexity, and in the bottom row, the mostcomplex, longer complaints in terms of their CDTs. The third columnshows detection accuracy for invalid argumentation in complaints in astand-alone argument validation system. Finally, the fourth column showsthe accuracy of the integrated argumentation extraction and validationsy stem.

In our validity assessment, we focus on target features (claims) relatedto what kind of verdict needs to be issued, such ascompensation_required, proceed_with_eviction, rent_receipt and others.System decision is determined by whether the identified claim isvalidated or not: if it is validated, then the verdict is in favor ofthis claim, and if not validated, decides against this claim.

In these results recall is low because in the majority of cases theinvalidity of claims is due to factors other than being self-defeated.Precision is relatively high since if a logical flaw in an argument isestablished, most likely the whole claim is invalid because otherfactors besides argumentation (such as false facts) contribute as well.As complexity of a complaint and its discourse tree grows, F1 firstimproves since more logical terms are available and then goes back downas there is a higher chance of a reasoning error due to a noisier input.

For decision support systems, it is important to maintain a low falsepositive rate. It is acceptable to miss invalid complaints, but for adetected invalid complaint, confidence should be rather high. If a humanagent is recommended to look at a given complaint as invalid, herexpectations should be met most of the time. Although F1-measure of theoverall argument detection and validation system is low in comparisonwith modern recognition systems, it is still believed to be usable as acomponent of a CRM decision-support system.

Detecting Distributed Incompetence

As discussed, certain aspects can identify distributed incompetence (DI)in text. Text can be transformed into a discourse representation ofarguments within the text. For example, aspects of the presentdisclosure can analyze whether a representation indicates a paragraphcommunicates both a claim and an argumentation that backs up the claim.A discourse tree is a means to express how author's thoughts areorganized in text. Its non-terminal nodes are binary rhetoricalrelations such as “elaboration” connecting terminal nodes associatedwith text fragments (called elementary discourse units).

Communicative discourse trees (CDTs) and/or machine learning models suchas classifiers can be used to detect distributed incompetence. In CDTs,the labels for communicative actions, which are added to the discoursetree edges, show which speech acts are attached to which rhetoricrelations. With distributed incompetence, activity such as persuasion isvery important in convincing a customer that banks are forced to demandinsufficient fund fees to maintain profitability. This form ofpersuasion is identified as argumentation. Argumentation needs a certaincombination of rhetorical relations of Elaboration, Contrast, Cause andAttribution to be sound. Persuasiveness relies on certain structureslinking Elaboration, Attribution and Condition. Explanation needs torely on certain chains of Elaboration relations plus Explanation andCause, and a rhetorical agreement between a question and an answer isbased on specific mappings between the rhetorical relations of Contrast,Cause, Attribution and Condition between the question and the answer.

Accordingly, to detect DI, invalid argumentation patterns used by theparties can be detected. In some cases, if arguments of only one partyare faulty, it does not necessarily mean a DI; however, if such anargumentation is systematic, it is natural to conclude that DI isoccurring. The systematic improper use of explainability indicates DI aswell.

In an environment rife with DI, agents have limited authorities oversolving problems and limited knowledge about the same of other agents.Passing a customer problem from one agent (who is a rational reasonerwithin the business domain) to another, a joint multiagent systemsometimes stops being a rational reasoner. In some cases, organizationssuch as insurance companies leverage DI as a means to retain income,trying to make customers give up on their existing claims. Somebusinesses rely on DI to avoid compensating customers for faultyproducts and services, in effect reversing transactions. In other cases,the upper management of an organization is not in a position to denycompensation, but the DI is a result of a lack of proper management. Inmany cases, customer support agents (CSAs) are not directly motivated tosolve customer problems, but instead, their performance is measured byan abstract user satisfaction score. Frequently, CSAs are either notuniformly motivated to perform their functions, or not motivated at all.Here is an example of how an external observer describes DI behaviorwith the terms of how a CSA describes his mission: “The only thing I amauthorized to do is to tell you that I am not authorized to doanything.”

Logically, DI can be inferred when a CSA demonstrates his intention todo something other than the well-being of a customer and his company atthe same time. Since there is frequently a conflict of interest betweena company and a customer, we cannot expect a CSA to always act in thebest interests of the customer. However, if a company's interests arenot satisfied either, one can conclude that DI is taking place.

When a customer describes her encounter with an individual CSA, she isfrequently dissatisfied even when her perception of her opponent isreasonable. However, what makes complainants appalled is aninconsistency between what different CSAs tell them about the samething. For example, what frequently happens is that one CSA explains theclient that his insufficient fund fee (NSF) is due to a too-earlywithdrawal transaction, whereas another CSA is saying that the deposittransaction has not gone through. This situation puts both client andcompany at disadvantage that is clearly indicative of a DI. Moreover,when a customer describes this kind of misinformation, it can be trustedin most cases since a customer would need to be too “inventive” tocompose a description with this form of inconsistency (it is much easierfor a dissatisfied customer to misrepresent an encounter with a singleagent).

Hence a DI can also be defined as a conflict between parties so thatthese parties behave irrationally by not acting in their best interestas perceived by an impartial judge reading a description of thisconflict. In part, a case here is a claimed conflict of interest whenthere is contradiction between the intents of the agents involved.Another case is where a conflict of interest is present but is notattempted to be resolved reasonably.

Some problems of a DI are associated with a limit on time set by oneagent involved in its communication. For example, in the healthcareindustry, doctors commonly interrupt patients explaining their problemsin 11 seconds on average. Having these reduced descriptions of aproblem, it is hard to make a competent decision; therefore, certainirrational reasoning patterns can be included, in particular, whenreferring to other specialist doctors.

A DI can be defined as an annotation framework as a decision by a humanexpert that a CSA has acted irrationally, contradicted another agent,demonstrated a lack of commonsense knowledge, or exhibited a substantiallack of knowledge about company rules or industry regulations. Anorganization with DI is irrational to an external observer but may wellbe rational from the expected utility standpoint of an organization'sCSA agents who minimize the compensation a user might achievecommunicating with such organization.

DI is a key reason customer complaints arise. People usually do notsubmit formal complaints because of their dissatisfaction with a productor service. To get to the point of a complaint's submission, usersusually have to be either handled badly by multiple CSAs or to encountera DI.

Examples of Distributed Incompetence

In a DI organization, the higher the level of management, the lessskillful and capable the respective manager has to be. To be a managerin a DI team, to operate it smoothly, a manager needs to possess agenuine incompetence and a lack of skills. To adequately control andguide lower-rank managers with limited skills and capabilities toproduce results, an upper-level manager needs to possess even lessskills. If an energetic, highly skilled and results-oriented managerfinds herself leading a team of personnel operating in a DI mode, shewould not fit in such a team. In a DI team, those members who are doerswould be left alone, and those who are not good at doing but who arewell in playing politics would be promoted to a managerial position toretain smooth DI operations. Hence, in a DI organization, people withlower delivering capabilities but better communication skills tend tooccupy management positions, increasing the stability of DI. Notice thatin our model, individual managers are not necessarily incompetent:instead, they lead the whole organization to the distributedincompetence state, possibly achieving their individual managerialgoals.

The financial crisis of 2008 provides an example of distributedincompetence. When residential mortgage-backed securities collapsed,investors, including federally insured financial institutions, hadbillions of dollars in losses. Each individual financial advisorunderstood the necessity to disclose the quality of securitized loans toclients. However, such disclosure would jeopardize his career and makeselling such shadow financial products more difficult. The most naturalway for a bank agent to communicate his attitude is to pretend that shedoes not understand the problems with financial products she is selling,and also pretend that she does not understand that her peers understandthe problem with financial products, and also pretend that she does notunderstand this pretense of others. The whole spectrum of financeprofessionals was hiding behind the curtains of DI, from bank clerks touniversity finance professors, to avoid being perceived asnon-professional. Financial crisis demonstrated how an organization canevolve from being a regular one where recommendations of their financialadvisors were reasonable and made sense, to a DI where those advisorspretended they did not understand how risky and meaningless theirrecommendations were. Not necessarily all advisors understood theproblems with their investment recommendations; some might havegenuinely believed that they were in the best interest of their clients.For a given bank employee, most of their managers and peers wereconfirming that their recommendations were valid, complying with bankpolicies (and maintaining the DI). A DI for an organization isstabilized if no employee wants to stand and blow whistle on highermanagement.

-   -   widespread failure in regulation and supervision;    -   dramatic failures of corporate governance and risk management at        many systemically important institutions;    -   a lack of transparency by service providers, ill preparation and        inconsistent action by higher-level management and        decision-making (such as government) that contribute to the        uncertainty and panic; and,    -   a systemic breakdown in accountability and ethics of the agents        involved;

Hence an organization with DI sooner or later leads to one or anotherform of crisis. This form of DI is associated with fairly complex mentalstates of agents:

-   -   pretend(agent, not know (agent, problem(fin_product)))    -   pretend(agent, not understand(agent, know (peer-agent,        problem(fin_product))))    -   pretend(agent, not understand(agent, pretend(peer-agent, not        know (peer-agent,    -   problem(fin_product)))))

A definition of pretend follows:

-   -   pretend(Agent, Pretense): −inform(Agent, Peer-agent, Pretense) &        believe(Agent, not Pretense)) & know (Peer-agent, not        believe(Agent, Pretense)).

A person (the Cognizer) comes to believe something (the Content),sometimes after a process of reasoning. This change in belief is usuallyinitiated by a person or piece of Evidence. Occasionally words in thisdomain are accompanied by phrases expressing Topic, i.e. that which themental Content is about.

DI and Other Forms of Irrationality

DI is a specific form of irrational behavior. The behavioral challengefor how agents make rational or irrational choices is associated with anindividual's decision-making. Behavioral irrationality does notnecessarily mean or leads to chaos: DI is a good example of it. Mostirrational behavior occurs in the course of a reasoning session, wheredecision makers do not behave with full knowledge and/or optimalcomputational power in pursuit of maximizing expected utility. In a DI,the behavior of agents is possibly rational for their personal expectedutility but definitely irrational for the expected utility of anexternal user or observer. Some critique the rationality paradigm forjudgments and preferences and for exploring the impact of culture onpeople's economic behavior. Moreover, the authors draw attention ofresearchers to the phenomenon of systemic irrationality. Irrationalitymay exist at the aggregate or societal level, a conclusion based on theobservation that large segments of the population are incapable ofmaking decisions in accord with traditional rationality—groups such asthose who have a psychiatric disorder, those who are taking medications,those with limited intelligence, those from the lower social classes,children and adolescents, and the elderly. Even those who are notincluded in these groups, but who take medications for medicalconditions may have their decision-making impaired to some extent.Therefore, it is argued that rationality in economic decision-making ismore frequently an exception rather than the norm.

Unlike other forms of irrationality, behavior of DI agents isexplainable and rational. For example, many agents may believe that theyare playing a positive role, having invented a reasonable explanation. Arational person's behavior is usually guided more by conscious reasoningthan by experience, and not adversely affected by emotion. An averagehuman is filled with systematic mistakes known to psychologists ascognitive biases. These mistakes affect most human decision-making.Cognitive bias makes people spend impulsively, be overly influenced bywhat other people think, and affects people's beliefs and opinions.

Irrational incompetence can also be considered from the standpoint of anunconscious. An unconsciously incompetent person is someone who does notrecognize they are doing something wrong and hence go on doing it. Forthem to unleash their full potential, they must first admit to thisincompetence and begin seeing the advantages of acquiring new skills. Anemployer can play an important role in adding this skill or competency.

DI can differ. When agents deviate from normal, rational behavior underthe orders of their managers, they are fully aware of what they aredoing. They know what they need to know and what they need to believe toperform their duties collectively to satisfy their DI goals. Customersupport agents in DI pretend they behave in an irrational way, so thatan observer of a DI team believes so, but they do not possess thefeatures of irrationality described above.

Distributed Incompetence and Artificial Intelligence

In general, for users, AI is proving too difficult to fully understand.So human and machine agents can focus their energy on either operatingAI systems or on understanding the underlying technology, but not bothat the same time. By embedding AI into the Internet of Things,particularly the intelligent infrastructure that will make up Internetof Things, humans and their technologists are creating a globaloperating system that is in a large sense opaque.

Users can use privacy as an excuse for DI. Although maintaining privacyis important for consumers, a lot of companies and governmentorganizations use privacy as their excuse for a DI. In healthcare,privacy—related legislation shifts the focus from a customer withmedical problems to privacy-related concerns brought upon this customer.It increases the amount of paperwork a customer needs to compete anddistracts his attention from the quality of health services beingprovided.

As customers are distracted from a health-related focus, a healthcareprovider can significantly increase profitability and efficiency of itsbusiness at the expense of customer well-being. Specialist doctors onlyspend 11 seconds on average listening to patients before interruptingthem, according to a new study (Singh et al. 2018). In primary carevisits, 49 percent of patients were able to explain their agenda, whilein specialty visits, only 20 percent of patients were allowed to explaintheir reason for visiting. For specialty care visits, however, eight outof 10 patients were interrupted even if they were allowed to share theiragenda.

Distributed Incompetence and Unexplainable Machine Learning

There are various implications related to a DI when organizations usemachine learning (ML) systems. If an ML system malfunctions and thecompany personnel cite it as a reason for an incompetent decision, anorganization easily slips into a DI. This slip is especially problematicif an ML system does not possess an explainability feature, its decisionare perceived as random, and thus are made in an incompetent way.

Although ML is actively deployed and used in industry, user satisfactionis still not very high in most domains. We will present a use case whereexplainability and interpretability of machine learning decisions islacking and users experience dissatisfaction in these cases.

FIG. 44 depicts an example of an interactive computing session inaccordance with an aspect. FIG. 44 depicts chat session 4400, whichincludes utterances 4401-4407. As depicted in utterance 4401, A customeris confused and his peers are upset when his credit card is canceled butno explanation is provided. The customer explains what happened indetail. As illustrated by utterances 4402-4407, his friends stronglysupport his case against the bank. Not only the banks made an error inits decision, according to what the friends write, but also it is unableto rectify it and communicate it properly. This situation is a clearindicator of a DI. If this bank used a decision-making system withexplainability, there would be a given cause of its decision. Once it isestablished that this cause does not hold, the bank is expected to becapable of reverting its decision efficiently and retaining thecustomer.

Computers attempting to be trustworthy is one potential reason for a DI.Incompetent workers can first start trusting machines and then blamethem for failures of mixed human-machine teams, if these machines lackexplainability. Lyons et al. (2019) present data from their qualitativestudy regarding the factors that precede trust for the elements ofhuman-machine teaming. The authors reviewed the construct ofhuman-machine trust and the dimensions of teammate-likeness from ahuman-robot interaction perspective. They derived the cues of trust fromthe corpus of Human-Computer Interaction literature on trust to revealthe reasons why individuals might have reported trust of a newtechnology, such as a machine. The authors found that most subjectsreported the technology as a tool rather than as a teammate forhuman-machine teaming.

Detecting DI in Text

The purpose of applying Natural Language Processing to DI phenomena isto find out the DI rate for different organizations. DI ratings obtainedfrom customer feedback texts can be an objective, unbiased assessment ofthe quality of management in a given organization, in most cases,irrespective of its particular policies and regulations. Onceorganizations are DI-rated, the public would be able to make an informedchoice of the products and services provided by them.

FIG. 45 depicts an exemplary communicative discourse tree for anargument in accordance with an aspect. FIG. 45 depicts CDT 4500, whichrepresents the text: ‘You are asking me to reverse this insufficientfund fee? I cannot do it. The only thing I<CSA> am allowed to do is totell you that I am not allowed to help you <the customer> with anything.I recommend you trying to ask a branch agent to reverse this fee foryou.’ This text can be viewed as a credo of a customer service agent(CSA).

Each line shows the text fragment for elementary discourse unit (UDU);expressions in italic are verb frames with substituted semantic roles.The hierarchy is shown from left to right: the level in the discoursetree is shown by the indentation value. The terminal nodes are assignedwith EDUs: the fragments of text which are connected by rhetoricalrelations. Edges of this discourse tree are labeled with speech actswhich are highlighted in EDUs, such as asking(you, me, . . . ).

The features of this discourse tree can be associated with a DI. Theabundance of speech acts and certain inter-relations between themindicate a peculiar mental state which should not arise unless amultiagent system evolves into a DI. For example, an inconsistencybetween allow( . . . ) and not allow( . . . ) connected by therhetorical relation of Attribution is a very special way of acontradiction which should not occur in the normal flow of a businessoperation, as expressed in this text.

Also, when the statement by a CSA ‘I cannot do’ is strengthened with theElaboration-Attribution chain, the reader believes that this customer isstuck with her problem and it is impossible for a CSA to provide anyhelp. This perception is the goal of an organization with a DI so that auser can easily give up on his attempts to resolve the matter.

Such texts can form examples for a training set. A classifier can detectcommonalities between communicative discourse trees for DI texts andautomatically builds rules for its detection. Not all such rules can beeasily verbalized but a special discourse tree structure is associatedwith these rules.

Examples of Linguistic Features that Indicate Distributed Incompetence

One of the linguistic patterns for DI is an entity loop, when one agent(entity) refers to another agent who then refers back. More precisely,the entity loop occurs when an agent or department Peter recommends tocontact John who in turns recommends Peter. This loop can be discoveredin an Entity—CDT with entity information.

FIG. 46 depicts a first exemplary communicative discourse tree thatincludes features indicative of distributed incompetence for an argumentin accordance with an aspect. FIG. 46 depicts CDT 4600. CDT representsthe following text: “Agent Peter from Customer Care recommended me toask agent John from Finance about how to refund my fee. Then when Iwrote to agent John, he told me to contact Customer Care.”

As can be seen in CDT 4600, corresponding entities are highlighted withrespective colors and the same entity arc is shown by arrows. Labels forthe edges of CDT encode the communicative actions with arguments asentities (agents). The loop relation between entities can form anelement of a positive training set for DI.

To identify the DI in the text depicted by FIG. 46, classificationapplication 102 identifies, by providing CDT 4600 to classifier 120, afirst communicative action 4601 (“recommend”) that identifies a firstentity as a first actor (“Peter”) and a second entity (“John”) as afirst recipient of the first communicative action. Classificationapplication 102 further identifies a second communicative action 4602(“recommend”) that identifies the second entity (“John”) as a secondactor and the first entity (“Peter”) as a second recipient of the secondcommunicative action.

In another example, DI can also be identified from an entity attributionchain. FIG. 47 shows one such example.

FIG. 47 depicts a second exemplary communicative discourse tree thatincludes features indicative of distributed incompetence for an argumentin accordance with an aspect. FIG. 47 depicts CDT 4700, which is derivedfrom the following text: “Agent Peter from Customer Care said that,according to Finance department, confirmation from management isrequired to refund my fee. John told me, that only the management candecide about the refund”

In this text there is no loop but the repetitive rhetorical relation ofattribution indicates that the Customer Service Agents (CSAs) arelacking authority and are citing a higher authority as a reason thencannot fulfill the customer request. As can be seen, embeddedattributions as an indication that an organization is formed in a way todeny customer requests by a reference to a higher authority. CSAs canview this as a more sound way of rejecting customer demands than justdenying without a reason.

To identify DI in the text depicted by FIG. 47, classificationapplication 102 provides CDT 4700 to classifier 120, which identifies inCDT 4700, a communicative action 4701 (“require”) that attributes anentity (“refund”) to a first actor (“management”). Communicative action4701 is associated with the “attribution” rhetorical relation.Classification application 102 further identifies, via classifier 120,in CDT 4700, a second communicative action 4702 (“decide”) thatattributes the entity (“refund”) to a second actor (“management”).

Another way to detect DI in text is to detect an explicit systematicdenial as per the labels of the edges of a CDT. FIG. 48 shows one suchexample.

FIG. 48 depicts a third exemplary communicative discourse tree thatincludes features indicative of distributed incompetence for an argumentin accordance with an aspect. FIG. 48 depicts CDT 4800, for thefollowing text: “Agent Peter from Customer Care said he could not refundmy fee. John told me, that he could not decide on his own concerning therefund. Agent Mike denied responsibilities when I was referred to him.”

Here, all three agents disagreed to do what they were asked by acustomer. When some CSA cannot help and some can, that is a case for aregular organization, and when all CSAs deny in one form or another,this is an indication of DI. As can be seen, the rhetorical relation ofattribution occurs here as well.

To identify DI in the text depicted by FIG. 48, classificationapplication 102 provides CDT 4800 to classifier 120, which identifies,in CDT 4800, a first communicative action 4801 that is of class “deny”and identifies a first actor (“Mike.”). For example purposes, the class“deny” includes at least the following communicative actions: reject,decline, ignore, not react, not respond, not pay attention, unhelpful,and inattentive.

Using Machine Learning to Detect Distributed Incompetence

FIG. 49 is a flowchart of an exemplary process 4900 for detectingdistributed incompetence in accordance with an aspect. Classificationapplication 102 can implement process 4900.

At block 4901, process 4900 involves accessing a body of text includingfragments. At least one fragment includes a verb and a plurality ofwords. Each word can include a role of the words within the fragment andeach fragment is an elementary discourse unit. Examples of suitablebodies of text include support logs and user utterances.

At block 4902, process 4900 involves generating a discourse tree thatrepresents rhetorical relationships between the fragments. At block4902, classification application 102 performs substantially similaroperations as performed at block 1502 of process 1500 described herein.

At block 4903, process 4900 involves matching each fragment that has averb to a verb signature. At block 4902, classification application 102performs substantially similar operations as performed at block1503-1505 of process 1500 described herein.

At block 4904, process 4900 involves building a communicative discoursetree by augmenting the fragments in the discourse tree with therespective matched verb signatures. Classification application 102augments each verb signature determined at block 4903 to a respectivefragment, thereby generating a communicative discourse tree.

At block 4905, process 4900 involves computing a probability of apresence of distributed incompetence in the body of text by applying apredictive model to the communicative discourse tree. Classifier 120 canimplement a predictive model. Examples of predictive models includeneural network, decision trees, and nearest neighbor classifiers.

The predictive model is trained to detect a level of distributedincompetence. For example, positive and negative classes are defined.The positive class includes text that indicates distributed incompetenceand the negative class includes text that does not indicate distributedincompetence. Standard learning techniques can be used.

Classifier 120 can be trained to detect one or more of the featuresdescribed with respect to figures FIGS. 46-48. For example, trainingdata can be obtained for a feature, including positive (with thefeature) and negative (without the feature) datasets. Classifier 120 canbe trained with the training data and used at block 4905. Classifier 120outputs a probability of the text including distributed incompetence.

At block 4906, process 4900 involves identifying the body of text ascontaining distributed incompetence responsive to determining that theprobability is past a threshold. Classifier 120 can output a probabilityof distributed incompetence. In some cases, classifier 120 can output abinary determination of a presence or absence of distributedincompetence.

At block 4907, process 4900 involves generating a response based on theidentification of distributed incompetence and inserting the generatedresponse into a conversation associated with the body of text.

Detecting Distributed Incompetence in Agent Specifications

Aspects of the present disclosure can detect distributed incompetence inagent specifications. Agent specifications include rules specified inlogic and/or text. As discussed, agent specifications include sets ofpreferred responses for an agent. Each response corresponds to a givenquestion. Agents can be human or automated.

An agent specification can be viewed as a set of logical rules thatdictate one or more responses, where each of the responses correspondsto a user input. For example if X utterance is received from a userdevice, then output Y answer. Agent specifications therefore typicallyset forth all likely scenarios (e.g., in the banking context, “what ismy balance?” and “please close my account”). A formal specificationlanguage can be used.

Collectively, the agent specifications can cause distributedincompetence. Classification application 102 can analyze a set of agentspecifications, where each agent specification corresponds to adifferent agent. The agents can have different roles. For example, afirst agent could correspond to a manager, a second to a customersupport agent, a third to a second-line customer support agent, and soforth.

If (a) each specification is internally irrational and (b) a conflictexists between the agents and the user, then distributed incompetencemay be present. In an example, classification application 102 firstdetermines that each specification is independently irrational.Determining irrationality involves identifying one or more rules pairsthat are in conflict with each other. For example, if a first rulestates “if a customer asks to have a fee reversed, then tell him I amnot authorized to do so” and a second rule states “if a customer asks tohave a fee reversed, then tell him to talk to another manager,” then aconflict exists.

Second, classification application determines whether a conflict existsbetween the specifications and the intentions (short-term) and desires(long term) of the user. Determining the user intentions involvesanalyzing each action of the user (e.g., utterances or actions taken).Determining the user's desires involves analyzing multiple utterancesand/or actions over multiple interactions and determining a commonalitybetween the utterances and/or actions.

If both conditions are met then distributed incompetence exists in theset of agent specifications.

Generation of Training Data

Manually-tagged sets of customer complaints from the financial sectorwere used for testing. Annotators were given a definition of a DI andhow to classify each complaint as indicative of a DI or not. Then webuilt a recognizer program that used this manually-tagged set fortraining and testing. Once our recognizer demonstrated satisfactoryperformance, we applied it to textual complaints for various banks toestimate their DI rate. Recognition accuracies in the manually-taggeddataset allowed us to estimate the value of deviation in the DI rate.This dataset contains texts where authors do their best to bring theirpoints across by employing all means to show that they (as customers)are right and their opponents (companies) are wrong. Customers alwaystry to blame the company for everything, so the task of the recognizeris to verify if customers' arguments are valid and their stories do notindicate misrepresentations. Complainants are emotionally chargedwriters who describe problems they have encountered with a financialservice, the lack of clarity and transparency as their problem wascommunicated with CSA, and how they attempted to solve it. Rawcomplaints were collected from PlanetFeedback.com for a number of bankssubmitted during the years of the Financial Crisis of 2007. Four hundredcomplaints were manually tagged with respect to perceived complaintvalidity, proper argumentation, detectable misrepresentation, andwhether a request for explanation concerning the company's decisionoccurred. Judging by these complaints, most complainants were in genuinedistress due to a strong deviation between:

-   -   what they expected from a product or a service;    -   the actual product or service that they received;    -   how this deviation was explained;    -   how the problem was communicated by a customer support.

The last two items are directly correlated with a DI. Most complaintauthors reported incompetence, flawed policies, ignorance, lack ofcommon sense, inability to understand the reason behind the company'sdecision, indifference to customers' needs and misrepresentation fromthe customer service personnel. The authors are frequently confused,looking for a company's explanation, seeking a recommendation from moreother users and advice others on avoiding a particular financialservice. The focus of a complaint is the proof that the proponent isright and her opponent is wrong, the explanation for why the companydecided to act in a certain way, a resolution proposal and a desiredoutcome. Although a DI is described in an indirect, implicit way, it canbe easily identified by a human reader.

The DI tag in the dataset used in the current study is related to thewhole text of a complaint, not a paragraph. Three annotators worked withthis dataset, and the inter-annotator agreement exceeded 80%. The set oftagged customer complaints about financial services is available at

Discourse-level features of Distributed Incompetence

In texts where a DI description might occur, one can expect specificdiscourse-level features. These texts can be an enumeration of thespecific mental states of CSA agents, an indication of the conflict witha lack of rationality, or heated arguments among conflicting agents,etc. It is important to differentiate between the emotions of a text'sauthor and the ones describing the mental states and communicativeactions of opponents. The complexity of a DI detection is increased bythe necessity of grasping the mental state of a team of agents, not anindividual one.

Cases of explanation requests and detection accuracies. The left columnpresent the linguistic cue for evidence of DI. The second column fromthe left gives the counts for each case. The third column presentscriteria and examples for the given evidence type. The fourth and fifthcolumns give the precision and recall recognizing the given evidencetype.

TABLE 14 Evidence # Criteria P R Expressions with the 83 Phrases: A saidthis . . . but B 83 85 rhetorical relation of said that . . . Contrast Ilearned from A one thing . . . but B informed me about something else.Double, triple, or more 97 Multiple rhetoric relation of 74 79 implicitmention of an Contrast, Explanation, Cause and inconsistency Sequence Asingle implicit mention of 103 A pair of rhetoric relation chains 69 75an inconsistency for contrast and cause

Detection accuracy for DI for different types of evidence is shown inTable 14. We consider simpler cases, where the detection occurs based onphrases, in the top row. Typical expressions in the row one have animperative form such as ‘please explain/clarify/motivate/comment’. Also,there are templates here such as ‘you did this but I expected that’ . .. ‘you told me this but I received that.’

The middle row contains the data on a level higher evidence for theimplicit explanation request case, where multiple fragments of DTsindicate the class. Finally, in the bottom row, we present the case oflower confidence for a single occurrence of a DT associated with anexplanation request. The second column shows the counts of complaintsper case. The third column gives examples of expressions (which includekeywords and phrase types) and rhetoric relations which serve ascriteria for an implicit DI. Fourth and fifth columns presents thedetection rates where the complaints for a given case is mixed with ahundred complaints without a DI.

Implementation of the discourse-level classifier

There are two approaches for discourse-level classification of textsinto classes {DI, no DI}:

-   -   1) Nearest neighbor learning. For a given text, if it is similar        from the discourse standpoint with an element of the positive        training dataset and dissimilar with all elements of the        negative dataset, then it is classified as belonging to the        positive class. The rule for the negative class is formulated        analogously. The similarity of two texts is defined as a        cardinality of maximal common discourse structure for the        respective discourse structures of these texts, such as        discourse trees.    -   2) The features of the discourse trees can be represented in a        numerical space. The kernel learning approach applies the        support vector machine (SVM) learning to the feature space of        all sub-discourse trees of the discourse tree for a given text        where a DI is being detected. Tree Kernel counts the number of        common sub-trees as the discourse similarity measure between two        DTs.        Both approaches are applied for DI detection; we refer the        reader to (Galitsky 2019) for details of both approaches and        briefly outline the latter approach below.

The tree kernel definition for the DT can be extended, augmenting the DTkernel by the information on speech acts. Tree kernel-based approachesare not very sensitive to errors in parsing (syntactic and rhetoric)because erroneous sub-trees are mostly random and will unlikely becommon among different elements of a training set.

A DT can be represented by a vector V of integer counts of each sub-treetype (without taking into account its ancestors):

V(T)=(# of subtrees of type 1, . . . , # of subtrees of type 1, . . . ,# of subtrees of type n). Given two tree segments DT₁ and DT₂, the treekernel function is defined:K (DT₁, DT₂)=<V(DT₁), V(CDT₂) >=Σi V(CDT₁)[i], V(DT₂)[i]=Σn₁Σn₂ Σ_(i)I_(i)(n₁)*I_(i)(n₂),where n₁∈N₁, n₂∈N₂ and N₁ and N₂ are the sets of all nodes in CDT₁ andCDT₂, respectively; I_(i)(n) is the indicator function:I_(i)(n)={1 iff a subtree of type i occurs with a root at a node; 0otherwise}. Further details for using TK for paragraph-level anddiscourse analysis are available in (Galitsky 2019).

Only the arcs of the same type of rhetoric relations (presentationrelation, such as antithesis, subject matter relation, such ascondition, and multinuclear relation, such as List) can be matched whencomputing common sub-trees. We use N for a nucleus or situationspresented by this nucleus, and S for a satellite or situations presentedby this satellite. Situations are propositions, completed actions oractions in progress, and communicative actions and states (includingbeliefs, desires, approve, explain, reconcile and others). Hence we havethe following expression for an RST-based generalization ‘{circumflexover ( )}’ for two texts text1 and text2:

text1{circumflex over ( )}text2=∪i, j (rstRelationIi,( . . . , . . .){circumflex over ( )}rstRelation2j ( . . . , . . . )),where I∈(RST relations in text1), j∈(RST relations in text2). Further,for a pair of RST relations their generalization looks as follows:rstRelation1(N1, S1){circumflex over ( )}rstRelation2 (N2,S2)=(rstRelation1{circumflex over ( )}rstRelation2)(N1{circumflex over( )}N2, S1{circumflex over ( )}S2).

Speech acts can be defined as a function of the form verb (agent,subject, cause), where verb characterizes some type of interactionbetween involved agents (e.g., explain, confirm, remind, disagree, deny,etc.), subject refers to the information transmitted or objectdescribed, and cause refers to the motivation or explanation for thesubject. To handle the meaning of words expressing the subjects ofspeech acts, we apply word2vec models.

Stanford NLP parsing, coreferences, entity extraction, DT construction(discourse parser, Surdeanu et al., 2016 and Joty et al., 2013), VerbNetand Tree Kernel builder can be expanded into one system.

For EDUs as labels for terminal nodes only the phrase structure isretained; the terminal nodes were labeled with the sequence of phrasetypes instead of parse tree fragments. For the evaluation, Tree Kernelbuilder tool was used (Galitsky 2019).

Detection Results

Once the plausibility of a DI detector on the annotated complaints wasconfirmed, the DI rate per organization was assessed The average DI rateper a customer complaint was 11%. Results are shown in the table below:

TABLE 15 Discovering DI rates for four banks DI Source # PrecisionRecall rate First bank 300 79 76 8.4 Second bank 300 76 80 11.6 Thirdbank 300 77 85 12.7 Fourth bank 300 76 84 11.2

Recognition accuracies and the resultant DI rates are shown in above. Weused 300 complaints for each bank to assess the recognition accuraciesfor explanation request. 79.1±3.1% looks like a reasonable estimate forrecognition accuracy for DI. The last column on the right shows thattaking into account the error rate that is less than 20% in DIrecognition, 10.9±3.1% is an adequate estimate of complaints indicatingDI, given the set of 1200 complaints. Hence the overall average DI ratefor these organizations is about one-tenth.

Handling and Repairing DI

DIs naturally appear in organizations due to the human factor. Hence themeans to cure a DI can be based on the removal of human factors: makingcustomer support fully formalized by following an established protocol.This approach follows along the lines of, for example, increased safetyby means of autonomous systems such as auto-pilots and navigators.Instead of dealing with human CSA from manifold motivations, customersshould be handled with an autonomous agent capable of understandingtheir problems in a limited, vertical domain.

As long as people rely on various products and services to satisfy theirneeds, they will encounter DIs associated with the frustration ofcustomers and with businesses losing customers. A transition to anautonomous CSA, as long as it is relevant in terms of topic and dialogueappropriateness, would make a DI avoidable. It is hard to overestimate apotential contribution of such a CSA when a broad category of peoplecall financial institutions, travel portals, healthcare and internetproviders, or government services such as immigration and revenueagencies.

Task—oriented chatbots for a customer service can provide adequatesolutions for a DI. Currently available dialogue systems (Galitsky andIlvovsky 2019) with dialogue management and context tracking can betrained from textual descriptions of customer problems and their correctresolution. The resultant functionality of these trained chatbots needsto be formally assessed to avoid hybrid human—machine DI.

The least typical cases of user dissatisfaction, such as the onesassociated with the non-sufficient fund fee, can be fully formalized andencoded into the CS chatbot so that human intervention would not berequired. A DI-free development team of chatbots should be able to coverthe most important cases of product issues and users' misunderstandingsto reduce the DI rate significantly from 11%.

Aspects of the present disclosure detect distributed incompetence (DI)from text. A comparison is drawn between DI and distributed knowledge:in both cases agents reason rationally, but in the former case theagents pretend to be irrational to achieve certain organizationalobjective so that an external agents would believe that he deals withgenuinely incompetent agents. In the latter case of distributedknowledge in a competent organization, knowledge and skills ofindividual agents help each other to impress an external observer with asuperior capability and result-oriented mindset of this organization.

It is not easy to detect distributed incompetence in an organization.Many banks during Financial crisis of 2007, ENRON as a public companyand also Theranos as a private company succeeded by leading investors bythe nose for a long time. Some company managers turn out to be so goodliars that neither employees nor customers nor members of the publicbecome suspicious about the company business conduct. The proposednatural language analysis tool is intended to take a corpus of documents(such as internal emails) from an organization and attempt to detect aDI. Our assessment showed that this tool could be plausible inidentifying a DI in an arbitrary organization.

FIG. 50 depicts a simplified diagram of a distributed system 5000 forimplementing one of the aspects. In the illustrated aspect, distributedsystem 5000 includes one or more client computing devices 5002, 5004,5006, and 5008, which are configured to execute and operate a clientapplication such as a web browser, proprietary client (e.g., OracleForms), or the like over one or more network(s) 5010. Server 5012 may becommunicatively coupled with remote client computing devices 5002, 5004,5006, and 5008 via network 5010.

In various aspects, server 5012 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. The services or software applications caninclude nonvirtual and virtual environments. Virtual environments caninclude those used for virtual events, tradeshows, simulators,classrooms, shopping exchanges, and enterprises, whether two- orthree-dimensional (3D) representations, page-based logical environments,or otherwise. In some aspects, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 5002, 5004, 5006, and/or5008. Users operating client computing devices 5002, 5004, 5006, and/or5008 may in turn utilize one or more client applications to interactwith server 5012 to utilize the services provided by these components.

In the configuration depicted in the figure, the software components5018, 5020 and 5022 of distributed system 5000 are shown as beingimplemented on server 5012. In other aspects, one or more of thecomponents of system distributed 5000 and/or the services provided bythese components may also be implemented by one or more of the clientcomputing devices 5002, 5004, 5006, and/or 5008. Users operating theclient computing devices may then utilize one or more clientapplications to use the services provided by these components. Thesecomponents may be implemented in hardware, firmware, software, orcombinations thereof. It should be appreciated that various differentsystem configurations are possible, which may be different fromdistributed system 5000. The aspect shown in the figure is thus oneexample of a distributed system for implementing an aspect system and isnot intended to be limiting.

Client computing devices 5002, 5004, 5006, and/or 5008 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 5002, 5004,5006, and 5008 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s)5010.

Although exemplary distributed system 5000 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 5012.

Network(s) 5010 in distributed system 5000 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 5010 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 5010 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.50 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 5012 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 5012 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 5012 using software defined networking. In variousaspects, server 5012 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 5012 may correspond to a server for performingprocessing described above according to an aspect of the presentdisclosure.

Server 5012 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 5012 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 5012 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 5002, 5004, 5006, and5008. As an example, data feeds and/or event updates may include, butare not limited to, Twitter® feeds, Facebook® updates or real-timeupdates received from one or more third party information sources andcontinuous data streams, which may include real-time events related tosensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like. Server 5012 may also include one or moreapplications to display the data feeds and/or real-time events via oneor more display devices of client computing devices 5002, 5004, 5006,and 5008.

Distributed system 5000 may also include one or more databases 5014 and5016. Databases 5014 and 5016 may reside in a variety of locations. Byway of example, one or more of databases 5014 and 5016 may reside on anon-transitory storage medium local to (and/or resident in) server 5012.Alternatively, databases 5014 and 5016 may be remote from server 5012and in communication with server 5012 via a network-based or dedicatedconnection. In one set of aspects, databases 5014 and 5016 may reside ina storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 5012 may be stored locallyon server 5012 and/or remotely, as appropriate. In one set of aspects,databases 5014 and 5016 may include relational databases, such asdatabases provided by Oracle, that are adapted to store, update, andretrieve data in response to SQL-formatted commands.

FIG. 51 is a simplified block diagram of one or more components of asystem environment 5100 by which services provided by one or morecomponents of an aspect system may be offered as cloud services inaccordance with an aspect of the present disclosure. In the illustratedaspect, system environment 5100 includes one or more client computingdevices 5104, 5106, and 5108 that may be used by users to interact witha cloud infrastructure system 5102 that provides cloud services. Theclient computing devices may be configured to operate a clientapplication such as a web browser, a proprietary client application(e.g., Oracle Forms), or some other application, which may be used by auser of the client computing device to interact with cloudinfrastructure system 5102 to use services provided by cloudinfrastructure system 5102.

It should be appreciated that cloud infrastructure system 5102 depictedin the figure may have other components than those depicted. Further,the aspect shown in the figure is only one example of a cloudinfrastructure system that may incorporate an aspect of the invention.In some other aspects, cloud infrastructure system 5102 may have more orfewer components than shown in the figure, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

Client computing devices 5104, 5106, and 5108 may be devices similar tothose described above for client computing devices 5002, 5004, 5006, and5008.

Although exemplary system environment 5100 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 5102.

Network(s) 5110 may facilitate communications and exchange of databetween client computing devices 5104, 5106, and 5108 and cloudinfrastructure system 5102. Each network may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including those described above for network(s) 5010.

Cloud infrastructure system 5102 may comprise one or more computersand/or servers that may include those described above for server 5012.

In certain aspects, services provided by the cloud infrastructure systemmay include a host of services that are made available to users of thecloud infrastructure system on demand, such as online data storage andbackup solutions, Web-based e-mail services, hosted office suites anddocument collaboration services, database processing, managed technicalsupport services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain aspects, cloud infrastructure system 5102 may include a suiteof applications, middleware, and database service offerings that aredelivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

Large volumes of data, sometimes referred to as big data, can be hostedand/or manipulated by the infrastructure system on many levels and atdifferent scales. Such data can include data sets that are so large andcomplex that it can be difficult to process using typical databasemanagement tools or traditional data processing applications. Forexample, terabytes of data may be difficult to store, retrieve, andprocess using personal computers or their rack-based counterparts. Suchsizes of data can be difficult to work with using most currentrelational database management systems and desktop statistics andvisualization packages. They can require massively parallel processingsoftware running thousands of server computers, beyond the structure ofcommonly used software tools, to capture, curate, manage, and processthe data within a tolerable elapsed time.

Extremely large data sets can be stored and manipulated by analysts andresearchers to visualize large amounts of data, detect trends, and/orotherwise interact with the data. Tens, hundreds, or thousands ofprocessors linked in parallel can act upon such data in order to presentit or simulate external forces on the data or what it represents. Thesedata sets can involve structured data, such as that organized in adatabase or otherwise according to a structured model, and/orunstructured data (e.g., emails, images, data blobs (binary largeobjects), web pages, complex event processing). By leveraging an abilityof an aspect to relatively quickly focus more (or fewer) computingresources upon an objective, the cloud infrastructure system may bebetter available to carry out tasks on large data sets based on demandfrom a business, government agency, research organization, privateindividual, group of like-minded individuals or organizations, or otherentity.

In various aspects, cloud infrastructure system 5102 may be adapted toautomatically provision, manage and track a customer's subscription toservices offered by cloud infrastructure system 5102. Cloudinfrastructure system 5102 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 5102 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 5102 isoperated solely for a single organization and may provide services forone or more entities within the organization. The cloud services mayalso be provided under a community cloud model in which cloudinfrastructure system 5102 and the services provided by cloudinfrastructure system 5102 are shared by several organizations in arelated community. The cloud services may also be provided under ahybrid cloud model, which is a combination of two or more differentmodels.

In some aspects, the services provided by cloud infrastructure system5102 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 5102. Cloud infrastructure system 5102 then performs processingto provide the services in the customer's subscription order.

In some aspects, the services provided by cloud infrastructure system5102 may include, without limitation, application services, platformservices and infrastructure services. In some examples, applicationservices may be provided by the cloud infrastructure system via a SaaSplatform. The SaaS platform may be configured to provide cloud servicesthat fall under the SaaS category. For example, the SaaS platform mayprovide capabilities to build and deliver a suite of on-demandapplications on an integrated development and deployment platform. TheSaaS platform may manage and control the underlying software andinfrastructure for providing the SaaS services. By utilizing theservices provided by the SaaS platform, customers can utilizeapplications executing on the cloud infrastructure system. Customers canacquire the application services without the need for customers topurchase separate licenses and support. Various different SaaS servicesmay be provided. Examples include, without limitation, services thatprovide solutions for sales performance management, enterpriseintegration, and business flexibility for large organizations.

In some aspects, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someaspects, platform services provided by the cloud infrastructure systemmay include database cloud services, middleware cloud services (e.g.,Oracle Fusion Middleware services), and Java cloud services. In oneaspect, database cloud services may support shared service deploymentmodels that enable organizations to pool database resources and offercustomers a Database as a Service in the form of a database cloud.Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain aspects, cloud infrastructure system 5102 may also includeinfrastructure resources 5130 for providing the resources used toprovide various services to customers of the cloud infrastructuresystem. In one aspect, infrastructure resources 5130 may includepre-integrated and optimized combinations of hardware, such as servers,storage, and networking resources to execute the services provided bythe PaaS platform and the SaaS platform.

In some aspects, resources in cloud infrastructure system 5102 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 5102 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain aspects, a number of internal shared services 5132 may beprovided that are shared by different components or modules of cloudinfrastructure system 5102 and by the services provided by cloudinfrastructure system 5102. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain aspects, cloud infrastructure system 5102 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one aspect, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 5102, and the like.

In one aspect, as depicted in the figure, cloud management functionalitymay be provided by one or more modules, such as an order managementmodule 5120, an order orchestration module 5122, an order provisioningmodule 5124, an order management and monitoring module 5126, and anidentity management module 5128. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 5134, a customer using a client device, such asclient computing device 5104, 5106 or 5108, may interact with cloudinfrastructure system 5102 by requesting one or more services providedby cloud infrastructure system 5102 and placing an order for asubscription for one or more services offered by cloud infrastructuresystem 5102. In certain aspects, the customer may access a cloud UserInterface (UI), cloud UI 5112, cloud UI 5114 and/or cloud UI 5116 andplace a subscription order via these Uls. The order information receivedby cloud infrastructure system 5102 in response to the customer placingan order may include information identifying the customer and one ormore services offered by the cloud infrastructure system 5102 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 5151, 5114 and/or 5116.

At operation 5136, the order is stored in order database 5118. Orderdatabase 5118 can be one of several databases operated by cloudinfrastructure system 5102 and operated in conjunction with other systemelements.

At operation 5138, the order information is forwarded to an ordermanagement module 5120. In some instances, order management module 5120may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 5140, information regarding the order is communicated to anorder orchestration module 5122. Order orchestration module 5122 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 5122 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 5124.

In certain aspects, order orchestration module 5122 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 5142, upon receiving an order for a newsubscription, order orchestration module 5122 sends a request to orderprovisioning module 5124 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 5124 enables the allocation of resources for the services orderedby the customer. Order provisioning module 5124 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 5102 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 5122 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 5140, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientcomputing devices 5104, 5106 and/or 5108 by order provisioning module5124 of cloud infrastructure system 5102.

At operation 5142, the customer's subscription order may be managed andtracked by an order management and monitoring module 5126. In someinstances, order management and monitoring module 5126 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain aspects, cloud infrastructure system 5102 may include anidentity management module 5128. Identity management module 5128 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 5102. In someaspects, identity management module 5128 may control information aboutcustomers who wish to utilize the services provided by cloudinfrastructure system 5102. Such information can include informationthat authenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 5128 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 52 illustrates an exemplary computer system 5200, in which variousaspects of the present invention may be implemented. The computer system5200 may be used to implement any of the computer systems describedabove. As shown in the figure, computer system 5200 includes aprocessing unit 5204 that communicates with a number of peripheralsubsystems via a bus subsystem 5202. These peripheral subsystems mayinclude a processing acceleration unit 5206, an I/O subsystem 5208, astorage subsystem 5218 and a communications subsystem 5224. Storagesubsystem 5218 includes tangible computer-readable storage media 5222and a system memory 5210.

Bus subsystem 5202 provides a mechanism for letting the variouscomponents and subsystems of computer system 5200 communicate with eachother as intended. Although bus subsystem 5202 is shown schematically asa single bus, alternative aspects of the bus subsystem may utilizemultiple buses. Bus subsystem 5202 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P5286.1standard.

Processing unit 5204, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 5200. One or more processorsmay be included in processing unit 5204. These processors may includesingle core or multicore processors. In certain aspects, processing unit5204 may be implemented as one or more independent processing units 5232and/or 5234 with single or multicore processors included in eachprocessing unit. In other aspects, processing unit 5204 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various aspects, processing unit 5204 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processingunit(s) 5204 and/or in storage subsystem 5218. Through suitableprogramming, processing unit(s) 5204 can provide various functionalitiesdescribed above. Computer system 5200 may additionally include aprocessing acceleration unit 5206, which can include a digital signalprocessor (DSP), a special-purpose processor, and/or the like.

I/O subsystem 5208 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system5200 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 5200 may comprise a storage subsystem 5218 thatcomprises software elements, shown as being currently located within asystem memory 5210. System memory 5210 may store program instructionsthat are loadable and executable on processing unit 5204, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 5200, systemmemory 5210 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 5204. In some implementations, system memory 5210 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system5200, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 5210 also illustratesapplication programs 5212, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 5214, and an operating system 5216. By wayof example, operating system 5216 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, andPalm® OS operating systems.

Storage subsystem 5218 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some aspects. Software (programs, codemodules, instructions) that when executed by a processor provide thefunctionality described above may be stored in storage subsystem 5218.These software modules or instructions may be executed by processingunit 5204. Storage subsystem 5218 may also provide a repository forstoring data used in accordance with the present invention.

Storage subsystem 5218 may also include a computer-readable storagemedia reader 5220 that can further be connected to computer-readablestorage media 5222. Together and, optionally, in combination with systemmemory 5210, computer-readable storage media 5222 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 5222 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible, non-transitorycomputer-readable storage media such as RAM, ROM, electronicallyerasable programmable ROM (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD), or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible computer readablemedia. When specified, this can also include nontangible, transitorycomputer-readable media, such as data signals, data transmissions, orany other medium which can be used to transmit the desired informationand which can be accessed by computer system 5200.

By way of example, computer-readable storage media 5222 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 5222 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 5222 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 5200.

Communications subsystem 5224 provides an interface to other computersystems and networks. Communications subsystem 5224 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 5200. For example, communications subsystem 5224may enable computer system 5200 to connect to one or more devices viathe Internet. In some aspects, communications subsystem 5224 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 802.28 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some aspects, communications subsystem 5224 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some aspects, communications subsystem 5224 may also receive inputcommunication in the form of structured and/or unstructured data feeds5226, event streams 5228, event updates 5252, and the like on behalf ofone or more users who may use computer system 5200.

By way of example, communications subsystem 5224 may be configured toreceive unstructured data feeds 5226 in real-time from users of socialmedia networks and/or other communication services such as Twitter®feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS)feeds, and/or real-time updates from one or more third party informationsources.

Additionally, communications subsystem 5224 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 5228 of real-time events and/or event updates 5252, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 5224 may also be configured to output thestructured and/or unstructured data feeds 5226, event streams 5228,event updates 5252, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 5200.

Computer system 5200 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 5200 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious aspects.

In the foregoing specification, aspects of the invention are describedwith reference to specific aspects thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, aspects can be utilized in any numberof environments and applications beyond those described herein withoutdeparting from the broader spirit and scope of the specification. Thespecification and drawings are, accordingly, to be regarded asillustrative rather than restrictive.

What is claimed is:
 1. A computer-implemented method for determining apresence of distributed incompetence by analyzing a communicativediscourse tree, the method comprising: accessing a body of textcomprising fragments, wherein at least one fragment comprises a verb anda plurality of words, each word comprising a role of the words withinthe fragment, wherein each fragment is an elementary discourse unit;generating a discourse tree that represents rhetorical relationshipsbetween the fragments, wherein the discourse tree comprises a pluralityof nodes, each nonterminal node representing a rhetorical relationshipbetween two of the fragments, each terminal node of the nodes of thediscourse tree is associated with one of the fragments; matching eachfragment that has a verb to a verb signature; building a communicativediscourse tree by augmenting the fragments in the discourse tree withthe respective matched verb signatures; computing a probability of apresence of distributed incompetence in the body of text by applying apredictive model to the communicative discourse tree, wherein thepredictive model is trained to detect a level of distributedincompetence; identifying the body of text as containing distributedincompetence responsive to determining that the probability is past athreshold; generating a response based on the identification ofdistributed incompetence; and inserting the generated response into aconversation associated with the body of text.
 2. The method of claim 1,further comprising: identifying, via the predictive model and in thecommunicative discourse tree, a first communicative action thatidentifies a first entity as a first actor and a second entity as afirst recipient of the first communicative action; and identifying, viathe predictive model and in the communicative discourse tree, a secondcommunicative action that identifies the second entity as a second actorand the first entity as a second recipient of the second communicativeaction.
 3. The method of claim 1, further comprising: identifying, viathe predictive model and in the communicative discourse tree, a firstcommunicative action that attributes an entity to a first entity,wherein the first communicative action is associated with an attributionrhetorical relation; and identifying, via the predictive model and inthe communicative discourse tree, a second communicative action thatattributes the entity to a second actor.
 4. The method of claim 1,further comprising: identifying, via the predictive model and in thecommunicative discourse tree, a first communicative action that is ofclass “deny” and identifies a first actor.
 5. The method of claim 1,wherein the matching comprises: accessing verb signatures, wherein eachverb signature comprises the verb of a respective fragment and asequence of thematic roles, wherein thematic roles describe arelationship between the verb and related words; determining, for eachverb signature, a plurality of thematic roles of the respectivesignature that matches a role of a word in the respective fragment;selecting a particular verb signature from the plurality of verbsignatures based on the particular verb signature comprising a highestnumber of matches; and associating the particular verb signature withthe fragment.
 6. The method of claim 4, wherein the associating furthercomprises: identifying each of the plurality of thematic roles in theparticular verb signature; and matching, for each of the plurality ofthematic roles in the particular verb signature, a corresponding word inthe respective fragment to the thematic role.
 7. The method of claim 1,wherein the verb is a communicative verb.
 8. A system comprising: anon-transitory computer-readable medium storing computer-executableprogram instructions; and a processing device communicatively coupled tothe non-transitory computer-readable medium for executing thecomputer-executable program instructions, wherein executing thecomputer-executable program instructions configures the processingdevice to perform operations comprising: accessing a body of textcomprising fragments, wherein at least one fragment comprises a verb anda plurality of words, each word comprising a role of the words withinthe fragment, wherein each fragment is an elementary discourse unit;generating a discourse tree that represents rhetorical relationshipsbetween the fragments, wherein the discourse tree comprises a pluralityof nodes, each nonterminal node representing a rhetorical relationshipbetween two of the fragments, each terminal node of the nodes of thediscourse tree is associated with one of the fragments; matching eachfragment that has a verb to a verb signature; building a communicativediscourse tree by augmenting the fragments in the discourse tree withthe respective matched verb signatures; computing a probability of apresence of distributed incompetence in the body of text by applying apredictive model to the communicative discourse tree, wherein thepredictive model is trained to detect a level of distributedincompetence; identifying the body of text as containing distributedincompetence responsive to determining that the probability is past athreshold; and generating a response based on the identification ofdistributed incompetence and inserting the generated response into aconversation associated with the body of text.
 9. The system of claim 8,wherein the operations further comprise: identifying, via the predictivemodel and in the communicative discourse tree, a first communicativeaction that identifies a first entity as a first actor and a secondentity as a first recipient of the first communicative action; andidentifying, via the predictive model and in the communicative discoursetree, a second communicative action that identifies the second entity asa second actor and the first entity as a second recipient of the secondcommunicative action.
 10. The system of claim 8, wherein the operationsfurther comprise: identifying, via the predictive model and in thecommunicative discourse tree, a first communicative action thatattributes an entity to a first entity, wherein the first communicativeaction is associated with an attribution rhetorical relation; andidentifying, via the predictive model and in the communicative discoursetree, a second communicative action that attributes the entity to asecond actor.
 11. The system of claim 8, wherein the operations furthercomprise: identifying, via the predictive model and in the communicativediscourse tree, a first communicative action that is of class “deny” andidentifies a first actor.
 12. The system of claim 11, wherein theassociating further comprises: identifying each of the plurality ofthematic roles in the particular verb signature; and matching, for eachof the plurality of thematic roles in the particular verb signature, acorresponding word in the fragment to the thematic role.
 13. The systemof claim 8, wherein the verb is a communicative verb.
 14. The system ofclaim 8, wherein each verb signature of the verb signatures comprisesone of (i) an adverb, (ii) a noun phrase, or (iii) a noun.
 15. Anon-transitory computer-readable storage medium storingcomputer-executable program instructions, wherein when executed by aprocessing device, the computer-executable program instructions causethe processing device to perform operations comprising: accessing a bodyof text comprising fragments, wherein at least one fragment comprises averb and a plurality of words, each word comprising a role of the wordswithin the fragment, wherein each fragment is an elementary discourseunit; generating a discourse tree that represents rhetoricalrelationships between the fragments, wherein the discourse treecomprises a plurality of nodes, each nonterminal node representing arhetorical relationship between two of the fragments, each terminal nodeof the nodes of the discourse tree is associated with one of thefragments; matching each fragment that has a verb to a verb signature;building a communicative discourse tree by augmenting the fragments inthe discourse tree with the respective matched verb signatures;computing a probability of a presence of distributed incompetence in thebody of text by applying a predictive model to the communicativediscourse tree, wherein the predictive model is trained to detect alevel of distributed incompetence; identifying the body of text ascontaining distributed incompetence responsive to determining that theprobability is past a threshold; and generating a response based on theidentification of distributed incompetence and inserting the generatedresponse into a conversation associated with the body of text.
 16. Thenon-transitory computer-readable storage medium of claim 15, wherein theoperations further comprise: identifying, via the predictive model andin the communicative discourse tree, a first communicative action thatidentifies a first entity as a first actor and a second entity as afirst recipient of the first communicative action; and identifying, viathe predictive model and in the communicative discourse tree, a secondcommunicative action that identifies the second entity as a second actorand the first entity as a second recipient of the second communicativeaction.
 17. The non-transitory computer-readable storage medium of claim15, wherein the operations further comprise: identifying, via thepredictive model and in the communicative discourse tree, a firstcommunicative action that attributes an entity to a first entity,wherein the first communicative action is associated with an attributionrhetorical relation; and identifying, via the predictive model and inthe communicative discourse tree, a second communicative action thatattributes the entity to a second actor.
 18. The non-transitorycomputer-readable storage medium of claim 15, wherein the operationsfurther comprise: identifying, via the predictive model and in thecommunicative discourse tree, a first communicative action that is ofclass “deny” and identifies a first actor.
 19. The non-transitorycomputer-readable storage medium of claim 18, wherein the associatingcomprises: identifying each of the plurality of thematic roles in theparticular verb signature; and matching, for each of the plurality ofthematic roles in the particular verb signature, a corresponding word inthe fragment to the thematic role.
 20. The non-transitorycomputer-readable storage medium of claim 15, wherein the verb is acommunicative verb.